Python/ Machine Learning Engineer (Regular/Senior)

🏢 Summary: The role focuses on building and owning robust data pipelines on a Spark cluster orchestrated with Airflow, while ensuring high data quality, observability, and reliable ML model deployment. You will work with Azure infrastructure using Infrastructure as Code and automate CI/CD workflows to support production-grade ML solutions. The position bridges data engineering and MLOps to deliver scalable, trustworthy systems in a retail loss prevention project. 🗂️ Requirements: Expert-level Python development, Strong experience with PySpark and large-scale data processing, Hands-on experience with Airflow for workflow orchestration, Experience provisioning cloud infrastructure using Infrastructure as Code, Proficiency with Azure or equivalent cloud platforms, Experience building and maintaining CI/CD pipelines with GitHub Actions, Experience with data quality validation and monitoring, Experience with observability and monitoring tools, Ability to productionize and monitor ML models, Advanced English (B2/C1) 📃 Skills: Python, PySpark, Airflow, Azure, Terraform, GitHub, Actions, CI/CD, Spark, Kubernetes, Grafana, Prometheus, MLOps 🏢 Description: We foster a dynamic culture rooted in strong engineering, a sense of ownership, and transparency, empowering our team. As part of the expanding VirtusLab Group, we offer a compelling environment for those seeking to make a substantial impact in the software industry within a forward-thinking organization. About the role You will be responsible for building and owning data pipelines on a Spark Kubernetes cluster orchestrated with Airflow using PySpark. You will improve and introduce data validation and monitoring to ensure trustworthy data at every stage. Tasks will include provisioning and managing Azure resources using a mature Infrastructure as Code approach, as well as automating everything with GitHub Actions and maintaining CI/CD workflows. You will enhance monitoring to further improve the reliability and stability of deployed ML solutions using the Grafana/Prometheus stack. Additionally, you will collaborate with cross functional teams to ensure the seamless deployment and serving of ML models and actively shape the project’s technical roadmap and direction. Python Expert Cloud (prefered Azure) Advanced IaC Regular GitHub Actions Regular Pyspark Regular Airflow Regular Experience with observability Nice to have MLOps: Proven ability to productionize models and set up monitoring Nice to have Dashboarding / visualization skills Nice to have Kubernetes Nice to have English Advanced Project Loss Prevention Project Scope Loss prevention in retail involves the strategic implementation of processes and technologies designed to identify, mitigate, and prevent the disappearance of inventory. To achieve that an Engineering and a Data Science team within a major UK retailer partner to bridge the gap between experimental ML models and robust, production-grade systems. By embedding engineering excellence into the data science lifecycle, the team ensures that loss prevention insights are delivered with high reliability. In this project you will not only develop high-quality Python code, but also implement trustworthy data pipelines on a big Spark cluster orchestrated with Airflow, setup highly automated CI/CD pipelines with Github Actions, and provision Azure infrastructure as code with Terraform. Tech Stack Python, PySpark, Airflow Azure, IaC (Terraform), CI/CD (Github Actions), Observability (Grafana/Promotheus), MLOps, Kubernetes Challenges Establish a resilient MLOps Ecosystem by integrating robust observability, experiment tracking and automated deployment to model serving infrastructure. Improve the reliability and observability of data pipelines to guarantee trust-worthy data. Advancing DevOps Maturity through the implementation of standardized pipelines, enabling rapid iteration and minimizing manual intervention. Team 3 Engineers What we expect in general: Strong experience in writing high-quality Python code and deploying production-level projects. Proactiveness and a strong sense of ownership, taking full responsibility of project outcomes. Significant experience in Data Engineering, specifically with PySpark, data quality monitoring and workflow orchestration. Proficiency in Azure (or equivalent cloud providers) and hands-on experience with Infrastructure as Code principles. Robust DevOps mindset with practical experience automating CI/CD pipelines via GitHub Actions. A dedicated team player with excellent communication skills who thrives within a cross-functional, collaborative environment. Good command of English (B2/C1 level), comfortable utilizing the language daily. A hybrid model is preferred (2-3 days per week in the Kraków office); alternatively, candidates must be available for on-site collaboration as required (approx. once a month). Seems like lots of expectations, huh? Don’t worry! You don’t have to meet all the requirements. What matters most is your passion and willingness to develop. Apply and find out! A few perks of being with us Building tech community Flexible hybrid work model Home office reimbursement Language lessons MyBenefit points Private healthcare Training Package Virtusity / in-house training And a lot more!

🏢 Summary: The offer is for an ML Engineer role focused on building and maintaining production-ready machine learning pipelines in an Azure-based cloud environment. The position involves deploying models, developing ML frameworks, and supporting forecasting and commodities projects using distributed data processing and DevOps practices. The role combines hands-on engineering with collaboration to standardize and scale AI solutions. 🗂️ Requirements: Strong experience in Python and production-level deployments, Experience with Azure cloud services, Experience with PySpark or other Spark-based distributed processing, Experience with Airflow or similar orchestration tools, Experience with Kubernetes ecosystem, Experience with Docker, Experience with Infrastructure as Code, Experience with CI/CD pipelines, Knowledge of MLOps and model monitoring, Experience with Git version control, Advanced English (B2/C1) 📃 Skills: Python, Azure, AzureML, PySpark, Spark, Airflow, Kubernetes, Docker, Terraform, Git, GitHub, GitHubActions, AzureDevOps, pandas, scikit-learn, numpy, MLOps, CI/CD, IaC 🏢 Description: We foster a dynamic culture rooted in strong engineering, a sense of ownership, and transparency, empowering our team. As part of the expanding VirtusLab Group, we offer a compelling environment for those seeking to make a substantial impact in the software industry within a forward-thinking organization. About the role Join our team to drive business innovation with production-ready machine learning pipelines. You will play a key role in deploying and maintaining ML workflows, leveraging Azure for cloud computing and on-prem clusters for ETLs. Collaborating closely with Data Scientists, you will contribute to AI-powered projects while shaping the organization’s technical culture. Python Advanced Cloud (prefered Azure) Advanced IaC Regular GitHub Actions Advanced Pyspark Regular Airflow Regular Experience with observability Nice to have MLOps: Proven ability to productionize models and set up monitoring Nice to have Productionize models and set up monitoring Regular Kubernetes Nice to have English Advanced View available projects Project Forecasting and Commodities Project Scope As an ML Engineer in Forecasting and Commodities, you will be involved in projects that support critical decision making processes, by applying your Python, PySpark, Kubernetes and Cloud (Azure) skills. You will be working in a technically mature ecosystem, implementing new features and covering new use-cases. Part of your responsibilities will be design and implementation of a data science innovation framework, as well making contributions to an overall engineering best practises of the organization. Your key responsibilities would be: Developing libraries, tools, and frameworks that standardise and accelerate development and deployment of machine learning models. Working in an Azure cloud environment, developing model training code in AzureML. Building and maintaining cloud infrastructure with IaC (infrastructure as code). Working with distributed data processing tools such as Spark, to parallelise computation for Machine Learning. Diagnosing and resolving technical issues, ensuring availability of high-quality solutions that can be adapted and reused. Collaborating closely with different engineering and data science teams, providing advice and technical guidance to streamline daily work. Championing best practices in code quality, security, and scalability by leading by example. Taking your own, informed decisions moving a business forward. Tech Stack Python, PySpark, Airflow, Docker, Kubernetes, Azure (incl. Azure ML), pandas, scikit-learn, numpy, GitHub Actions, Azure DevOps, Terraform, Git, GitHub. Project Challenges Building a system that provides accurate and up-to-date business forecasts, by providing a set of tools that can be easily leveraged by data scientists and analysts. Streamlining the process of onboarding, deployment and patching new ML pipelines. Collaborating with cross-functional teams enhancing customer experiences through innovative technologies. Employing DevOps practises for reproducible patterns in multiple business domains. Team 3 engineers What we expect in general: Strong experience in writing high-quality Python code and deploying production-level projects. Experience with orchestration tools such as Airflow. Knowledge of Spark or other distributed data processing tools. Experience with Kubernetes ecosystem as a user. Strong experience in Cloud (preferred Azure) and Docker Ability to work in a team and participate in the design process. Strong communicator. Team player with mentoring ability. Proactive and responsible Strategic thinker with big-picture perspective Good command of English (B2/C1). A hybrid model is preferred (2-3 days per week in the Kraków office); alternatively, candidates must be available for on-site collaboration as required (approx. once a month). Seems like lots of expectations, huh? Don’t worry! You don’t have to meet all the requirements. What matters most is your passion and willingness to develop. Apply and find out! A few perks of being with us Building tech community Flexible hybrid work model Home office reimbursement Language lessons MyBenefit points Private healthcare Training Package Virtusity / in-house training And a lot more!

🏢 Summary: The role focuses on building and owning reliable data pipelines and MLOps infrastructure for a retail loss prevention project using Spark, Airflow, and Azure. You will develop production-grade Python solutions, automate CI/CD processes, and provision cloud infrastructure as code while enhancing monitoring and observability. The position bridges data science and engineering to deliver robust, scalable ML systems in production. 🗂️ Requirements: Strong experience with Python in production environments, Hands-on experience with PySpark and Spark clusters, Experience with workflow orchestration using Airflow, Experience with Azure or equivalent cloud platforms, Practical experience with Infrastructure as Code, Experience automating CI/CD pipelines with GitHub Actions, Knowledge of data validation and monitoring practices, Experience with Kubernetes in production, Experience with observability and monitoring tools, Upper-intermediate English (B2/C1) 📃 Skills: Python, PySpark, Spark, Airflow, Azure, Terraform, GitHub, CI/CD, Kubernetes, Grafana, Prometheus, MLOps 🏢 Description: We foster a dynamic culture rooted in strong engineering, a sense of ownership, and transparency, empowering our team. As part of the expanding VirtusLab Group, we offer a compelling environment for those seeking to make a substantial impact in the software industry within a forward-thinking organization. About the role You will be responsible for building and owning data pipelines on a Spark Kubernetes cluster orchestrated with Airflow using PySpark. You will improve and introduce data validation and monitoring to ensure trustworthy data at every stage. Tasks will include provisioning and managing Azure resources using a mature Infrastructure as Code approach, as well as automating everything with GitHub Actions and maintaining CI/CD workflows. You will enhance monitoring to further improve the reliability and stability of deployed ML solutions using the Grafana/Prometheus stack. Additionally, you will collaborate with cross functional teams to ensure the seamless deployment and serving of ML models and actively shape the project’s technical roadmap and direction. Project: Loss Prevention Project Scope Loss prevention in retail involves the strategic implementation of processes and technologies designed to identify, mitigate, and prevent the disappearance of inventory. To achieve that an Engineering and a Data Science team within a major UK retailer partner to bridge the gap between experimental ML models and robust, production-grade systems. By embedding engineering excellence into the data science lifecycle, the team ensures that loss prevention insights are delivered with high reliability. In this project you will not only develop high-quality Python code, but also implement trustworthy data pipelines on a big Spark cluster orchestrated with Airflow, setup highly automated CI/CD pipelines with Github Actions, and provision Azure infrastructure as code with Terraform. Tech Stack Python, PySpark, Airflow Azure, IaC (Terraform), CI/CD (Github Actions), Observability (Grafana/Promotheus), MLOps, Kubernetes Challenges Establish a resilient MLOps Ecosystem by integrating robust observability, experiment tracking and automated deployment to model serving infrastructure. Improve the reliability and observability of data pipelines to guarantee trust-worthy data. Advancing DevOps Maturity through the implementation of standardized pipelines, enabling rapid iteration and minimizing manual intervention. Team 3 Engineers What we expect in general: Strong experience in writing high-quality Python code and deploying production-level projects. Proactiveness and a strong sense of ownership, taking full responsibility of project outcomes. Significant experience in Data Engineering, specifically with PySpark, data quality monitoring and workflow orchestration. Proficiency in Azure (or equivalent cloud providers) and hands-on experience with Infrastructure as Code principles. Robust DevOps mindset with practical experience automating CI/CD pipelines via GitHub Actions. A dedicated team player with excellent communication skills who thrives within a cross-functional, collaborative environment. Good command of English (B2/C1 level), comfortable utilizing the language daily. A hybrid model is preferred (2-3 days per week in the Kraków office); alternatively, candidates must be available for on-site collaboration as required (approx. once a month). Seems like lots of expectations, huh? Don’t worry! You don’t have to meet all the requirements. What matters most is your passion and willingness to develop. Apply and find out! A few perks of being with us Building tech community Flexible hybrid work model Home office reimbursement Language lessons MyBenefit points Private healthcare Training Package Virtusity / in-house training And a lot more!

🏢 Summary: Machine Learning Engineer role focused on deploying and maintaining production-ready ML pipelines and computer vision solutions on retail edge devices. The position involves building scalable cloud and edge architectures, automating ML workflows, and ensuring reliable deployment across thousands of devices using Azure and Python-based technologies. The role also includes collaboration with Data Scientists and contributing to AI-powered projects through monitoring, optimization, and technical consulting. 🗂️ Requirements: 5+ years of software engineering experience, Bachelor’s or Master’s degree in Computer Science or related field, Expert-level Python skills, Advanced Airflow experience, Advanced Docker experience, Advanced GitHub Actions experience, Strong Azure cloud expertise, Ability to work independently, English proficiency at B2/C1 level, Availability for hybrid work model 📃 Skills: Python, Azure, Docker, Airflow, GitHub, DeepStream, Streamlit, PostgreSQL, Terraform, ETL, CCTV 🏢 Description: VirtusLab is a leading European software consulting and engineering company. Our mission is to craft clean code and practical solutions with precision and purpose. We foster a dynamic culture rooted in strong engineering, a sense of ownership, and transparency, empowering professionals to make a substantial impact in the software industry. About the role Driving business innovation through production-ready machine learning pipelines. Collaboration focuses on deploying and maintaining ML workflows while leveraging Azure for cloud computing and on-prem clusters for ETLs. The scope of services involves close cooperation with Data Scientists, active contribution to AI-powered projects, and consulting on technical solutions and translating complex details for business stakeholders. Business delivery is achieved through the independent deployment and monitoring of models in production, combined with building and maintaining analytical dashboards for continuous insights. Furthermore, the engagement encompasses driving technical excellence across the project environment by fostering knowledge sharing and delivering high-level technical sessions. Python Expert Cloud (prefered Azure) Advanced Docker Advanced Airflow Advanced GitHub Actions Advanced English Advanced View available projects Project Computer Vision / Integration Project Scope The scope of services for the Machine Learning Engineer involves bridging the gap between data science and operational deployment at scale, focusing on the integration and automation of Machine Learning models directly onto retail edge devices, such as self-checkout tills. The core delivery includes: Development and maintenance of components for processing CCTV data, leveraging technologies like NVidia Deepstream. Streamlining the data onboarding process to enable the creation of specialized solutions by Data Science units. Designing and implementing scalable, high-quality code and architectures within cloud and edge environments. Optimization of data storage and processing in the cloud, selecting the most effective tools and architectures to meet business requirements. Enhancing the monitoring, reliability, and stability of deployed solutions, including the delivery of automated testing suites and simulation environments for edge devices. Fostering technical alignment with cross-functional units to ensure the seamless integration of ML models with retail tills, driving customer experience enhancement through innovative technologies. Tech Stack Python, Docker, DeepStream, Airflow, Azure, GitHub Actions, Streamlit, PostgreSQL, Terraform Project Challenges – Deploying and monitoring deployment to 10,000 devices – Ensuring similar behaviour on all devices at the same time – Dealing with internet interruptions during deployment – Dealing with the raw scale of data from thousands of cameras Project environment 3x Python Engineers, without an SDM, operating mostly in the larger Computer Vision team that, apart from engineers, consists of three Data Scientists, a Data Science Manager and Product Manager. The Engineering Team is distributed in Poland while the broader team is distributed in Poland and the United Kingdom. What we expect in general: Minimum 5 years of general software engineering experience with master’s or bachelor’s degree in Computer Science or related field Expert-level Python programming skills and extensive hands-on experience with Airflow, Docker, and GitHub Actions Strong Azure cloud expertise combined with ownership mindset and proven ability to work independently Good command of English (B2/C1). Availability to work in a hybrid model from time to time. Seems like lots of expectations, huh? Don’t worry! You don’t have to meet all the requirements. What matters most is your passion and willingness to develop. Apply and find out! A few perks of being with us Building tech community Flexible hybrid work model Home office reimbursement Language lessons MyBenefit points Private healthcare Training Package Virtusity / in-house training Access to the above perks is optional and completely voluntary for B2B contractors Apply now

🏢 Summary: Opportunity to drive business innovation by building and deploying production-ready machine learning pipelines in a hybrid cloud environment for a major UK retailer. The role focuses on end-to-end ML lifecycle implementation, Azure-based infrastructure provisioning, and developing scalable data pipelines and APIs for price optimisation solutions. You will collaborate closely with data scientists and engineers to productionise models and ensure robust monitoring and DevOps practices. 🗂️ Requirements: Strong experience in Python development, Hands-on experience with full Machine Learning Lifecycle implementation, Experience with Infrastructure as Code for cloud resource provisioning, Experience building data pipelines, preferably with PySpark, Knowledge of production system monitoring, Good English proficiency (B2/C1), Availability for hybrid work model 📃 Skills: Python, PySpark, Airflow, Azure, AzureML, Terraform, Spark, Kubernetes, FastAPI, MLflow, CI/CD, GitHub, Docker, Splunk 🏢 Description: VirtusLab is a leading European software consulting and engineering company. Our mission is to craft clean code and practical solutions with precision and purpose. We foster a dynamic culture rooted in strong engineering, a sense of ownership, and transparency, empowering professionals to make a substantial impact in the software industry. About the role Driving business innovation through production-ready machine learning pipelines. Collaboration focuses on deploying and maintaining ML workflows while leveraging Azure for cloud computing and on-prem clusters for ETLs. The scope of services involves close cooperation with Data Scientists, active contribution to AI-powered projects, and consulting on technical solutions and translating complex details for business stakeholders. Business delivery is achieved through the independent deployment and monitoring of models in production, combined with building and maintaining analytical dashboards for continuous insights. Furthermore, the engagement encompasses driving technical excellence across the project environment by fostering knowledge sharing and delivering high-level technical sessions. Project Scope Delivering price optimisation solutions in close cooperation with a major UK retailer’s data science team. The project aims to enable quick exploration and productionisation of the ML models and respective optimisation algorithms in a hybrid-cloud environment. The ultimate objective is providing APIs for the optimiser to solve pricing class problems across multiple business domains. The scope of cooperation covers: Implementing the end-to-end Machine Learning Lifecycle, starting from data preparation through automated deployment to continuous monitoring of data and models in production. Developing PySpark data pipelines to load and transform large amounts of data to produce significant features used in modelling and analytics. Provisioning all cloud resources supporting model development in AzureML with IaC using Terraform Selecting the best architectural patterns to solve business problems. Building robust and maintainable code in the cloud and on-prem to bring models fast and reliably to production Establishing a mature DevOps culture and developing solutions reusable in multiple business domains. Tech Stack Python (PySpark, Airflow, Azure SDK, fastapi, mlflow), Spark on K8S, Azure ML and IaC with Terraform, CI/CD with GitHub Actions, Docker, Splunk. Challenges Addressing numerous pricing problems in the global retailer world sharing similar structures and constraints. The focus is on building robust solutions reusable across multiple domains, leveraging a hybrid on-prem and cloud infrastructure, and ensuring top quality while maintaining quick iterations. Project Environment The project delivery involves a core engineering team of 5-7 professionals in Poland, collaborating closely with the client’s product management, data science team, and internal engineers. What we expect in general: Strong track record in writing high-quality Python code. Hands-on experience with implementing different stages of the Machine Learning Lifecycle. Experience with Infrastructure as Code to set up Cloud resources. Experience with creating data pipelines (ideally with PySpark). Solid understanding of the importance of monitoring in production systems. Good command of English (B2/C1). Availability to work in a hybrid model from time to time. Seems like lots of expectations, huh? Don’t worry! You don’t have to meet all the requirements. What matters most is your passion and willingness to develop. Apply and find out! A few perks of being with us Building tech community Flexible hybrid work model Home office reimbursement Language lessons MyBenefit points Private healthcare Training Package Virtusity / in-house training Access to the above perks is optional and completely voluntary for B2B contractors

🏢 Summary: The role focuses on engineering production-grade AI features such as semantic search, intelligent assistants, and automated risk scoring by integrating external AI providers into a cybersecurity platform. You will own the full lifecycle from prototype to reliable, monitored production services, primarily using Python on Azure. This position emphasizes robust software engineering practices over model research or training. 🗂️ Requirements: 5+ years of professional experience building production systems in Python, Expert-level Python programming skills, Experience with ML frameworks and libraries, Proven experience shipping and operating production software, Experience integrating external APIs in production environments, Strong understanding of software architecture and design patterns, Practical experience with CI/CD pipelines, Experience with containerization using Docker, Understanding of AI/ML concepts including LLMs, embeddings, prompt engineering, RAG, Experience with unit and integration testing and mocking external services, Polish work permit, Professional proficiency in English 📃 Skills: Python, Azure, OpenAI, Anthropic, Docker, CI/CD, FastAPI, Flask, Pinecone, Weaviate, Chroma, .NET, REST, LLMs, RAG, Embeddings, Git 🏢 Description: About the Role As a Senior AI/ML Engineer you will be building AI-powered features at the core of our platform - semantic search, an intelligent assistant, automated risk scoring, and much more. We mainly integrate with external AI model providers (OpenAI, Anthropic, and others) to deliver these capabilities to our clients and internal users. This is a software engineering role, not a data science or ML research position. You won't train models from scratch or publish papers. You'll engineer production-grade systems that leverage AI to solve real cybersecurity problems. If you're a strong Python engineer excited about making AI reliable, maintainable, and impactful — we want to talk to you. We're looking for a versatile software engineer to own the journey from AI prototype to reliable production system: building robust integrations with external AI providers or fine-tuned models, standardizing our Python codebase and release pipelines, and ensuring our AI features meet the same quality bar as any other production service. What You'll Do Ship AI features end to end - from integration design through deployment to monitoring in production. You'll build and maintain Python services that power our AI-driven capabilities on Azure. Build production-ready AI / ML integrations - including retry logic, rate limiting, cost monitoring, fallback strategies, and abstraction layers that allow us to swap between model providers. Turn proofs of concept into reliable software - collaborate with our AI experts (who bring deep ML/data science expertise) to take working prototypes and engineer them into tested, documented, production-grade services. Standardize Python engineering practices - establish and evolve our code organization, testing strategies, CI/CD pipelines, and release processes for AI/ML features. Design clean service boundaries - our platform's core runs on .NET. You'll design and maintain well-defined APIs between the Python/AI layer and the rest of the system. Mentor and share knowledge - help other engineers grow their Python and AI engineering skills, and bring engineering rigor to the broader team's practices. Contribute to architecture decisions - work closely with other teams to shape how AI capabilities evolve within the platform. Who You Are Must-Haves 🔹 5+ years of professional experience building and maintaining production systems in Python. 🔹 Expert-level programming skills and proficiency with common ML frameworks and libraries. 🔹 A track record of shipping and operating software - not just building prototypes. You care about testing, monitoring, logging, and what happens after deployment. 🔹 Experience integrating external APIs in production systems - ideally AI/LLM provider APIs (OpenAI, Anthropic, Azure AI, or similar), but strong API integration experience in any domain counts. 🔹 Solid understanding of software architecture and design patterns - you have opinions about how to structure a Python project and can articulate trade-offs. 🔹 Practical knowledge of CI/CD pipelines, containerization (Docker), and modern development workflows. 🔹 A working understanding of AI/ML concepts - you know what embeddings, LLMs, prompt engineering, and RAG are. You can have a productive technical conversation with an ML specialist without needing every concept explained from scratch. 🔹 Experience with testing strategies - unit tests, integration tests, mocking external services. You instinctively write tests, not as an afterthought. 🔹 A highly adaptable, problem-solving and result-oriented mindset with the ability to work independently and take ownership in a fast-paced and dynamic environment. 🔹 Strong communication skills in English (our working language). 🔹 Willingness to learn continuously and share what you learn with the team. Nice-to-Haves 🔸 Experience with Azure cloud services. 🔸 Familiarity with FastAPI, Flask, or similar Python web frameworks. 🔸 Exposure to MLOps tooling or practices (model versioning, experiment tracking, feature stores). 🔸 Hands-on experience in Document AI / Intelligent Document Processing using traditional models and Generative AI. 🔸 Experience with vector databases (Pinecone, Weaviate, Chroma, or similar). 🔸 Any experience with .NET - helpful for cross-team collaboration, but not expected. 🔸 Background in or interest in cybersecurity as a domain. Your Team You'll join our Synapse team - a small, focused group responsible for all AI/ML capabilities within our platform. The team consists of: -> Team Leader / Architect - provides high-level architectural guidance, planning, and cross-stack delivery. -> AI / ML Engineer - deep expertise in AI/ML and data science. Your counterbalance: they bring the ML knowledge, you bring the production engineering discipline. -> AI / Backend Engineer - transitioning from .NET into AI engineering. A teammate you'll mentor and grow alongside. -> You - the person who bridges the gap between AI prototypes and reliable production systems. Why Join Us? / What We Offer: Competitive salary and benefits package. Remote work options and flexible working hours. Actual impact on the choice and shape of solutions developed. Opportunities for professional growth and development. Training and conference budget. A collaborative, innovative work environment with an iterative agile approach. The role is for six months with the possibility of extension. Both full-time and part-time cooperation are possible.

🏢 Summary: Contract role focused on building and productionising Python-based data and ML systems that support front-office portfolio management and analytics. Involves designing scalable data pipelines, modernising legacy workflows, and delivering reliable solutions integrated with enterprise data platforms. High-impact position working closely with investment teams to enable proprietary models and core analytics. 🗂️ Requirements: Strong professional experience with Python engineering, Experience building ETL or ML-oriented Python workflows, Experience developing robust data pipelines, Experience working with parquet or columnar data formats, Experience with PySpark or distributed data processing, Experience working in Databricks or similar platform environments, Proficiency with Git, Experience with testing and code reviews, Experience with CI/CD pipelines, Understanding of software engineering best practices 📃 Skills: Python, ETL, ML, Parquet, PySpark, Spark, Databricks, Git, CI/CD, Testing, APIs, Containers 🏢 Description: Location: Flexible (Hybrid in Wroclaw) Department: Front‑Office Technology & Analytics Type: Contract About the Role We’re working with a leading global investment organisation that is expanding its front‑office engineering function. This role sits at the intersection of data engineering, applied data science, and front‑office portfolio support , with a focus on building high‑quality Python systems that directly enable portfolio managers, analysts, and risk teams. You’ll work across several investment teams, helping to rewrite key components, productionise POCs, optimise data access patterns, and deliver reliable, scalable pipelines that power core analytics and proprietary models. If you enjoy solving complex data problems, building production systems end‑to‑end, and working closely with front‑office stakeholders, this is a high‑impact opportunity. What You’ll Work On Design, build, and operate Python-based data pipelines (batch & near‑real‑time) reading/writing to governed enterprise storage (e.g., parquet patterns via internal cloud storage). Develop production‑ready ML/data components including feature prep, scoring pipelines, and API‑exposed data services. Support the re‑engineering and migration of legacy workflows into modern Python-based solutions. Partner directly with front‑office portfolio managers and analysts to extend and industrialise proprietary models. Optimise Databricks connectivity and collaborate with platform teams to improve Spark/PySpark workloads. Support portfolio teams with data access, connectivity principles, and technical design for productionisation. Contribute to engineering standards: CI/CD, testing, code reviews, observability, automation, and documentation. Work on POCs through to full production rollout, including rewriting logic from other languages into Python where required. What We’re Looking For Must‑Have Skills Strong, professional-level Python engineering skills (data structures, packaging, typing). Experience with ETL or ML‑oriented Python workflows. Experience building robust data pipelines and working with parquet/columnar formats in governed storage. Exposure to PySpark or distributed data processing . Comfortable working in platform environments (Databricks, containerised services, internal dev platforms). Strong software engineering foundations: Git, testing, code review, CI/CD , documentation.

🏢 Summary: Platform Engineer role focused on designing, building, and maintaining Azure-based cloud infrastructure, Kubernetes clusters, and shared CI/CD pipelines. The position centers on Azure DevOps pipeline templates, Infrastructure as Code with Terraform, and automation for .NET application deployments. The role ensures secure, standardized, and reliable platform operations across environments. 🗂️ Requirements: Hands-on experience with Azure Kubernetes Service (AKS) configuration and operations, Strong knowledge of CI/CD principles and GitFlow branching strategy, Experience building and maintaining Azure DevOps YAML pipelines, Proficiency in PowerShell Core 7+ scripting and module development, Experience with Terraform modules and state management, Experience with Azure services (Key Vault, ACR, Service Bus, SQL Database, Storage, Managed Identities, RBAC), Knowledge of .NET build and containerization processes, Understanding of Azure networking (VNets, NSGs, private endpoints, DNS, ingress), Experience managing multi-stage deployment pipelines, Ability to design and maintain Infrastructure as Code solutions 📃 Skills: Azure, AKS, AzureDevOps, YAML, Terraform, PowerShell, Kubernetes, Helm, Docker, ACR, .NET, NuGet, SQL, DACPAC, ServiceBus, KeyVault, RBAC, ManagedIdentities, VNets, NSG, DNS, OpenShift, SonarQube, Keycloak, AzureCLI 🏢 Description: About the Role The Platform Engineer is a key member of the Platform Team, responsible for enabling development, deployment, and operation of applications across the organization. You will design, build, and maintain the shared foundations — pipelines, infrastructure, and tooling — that development teams rely on to ship software faster, more securely, and more reliably. You will own and evolve a shared Azure DevOps pipeline template library consumed by all application teams, manage Azure Kubernetes Service (AKS) clusters (including confidential compute), and automate infrastructure provisioning using Infrastructure as Code. Key Responsibilities Platform Engineering & Infrastructure Design, build, and maintain Azure Kubernetes Service (AKS) clusters — both private and public — including node pool configuration, networking, ingress, autoscaling, and confidential compute workloads. Manage and evolve the Azure cloud platform: subscriptions, resource groups, Key Vault, Azure Container Registry, Service Bus, Storage Accounts, and SQL databases. Operate and support OpenShift environments where applicable. Ensure platform security, reliability, and compliance with organizational standards (network policies, managed identities, TLS, RBAC). CI/CD Pipeline Development & Maintenance Develop and maintain shared, reusable CI/CD pipeline templates (YAML) in Azure DevOps, consumed by all application repositories across the organization. Build and optimize multi-stage deployment pipelines covering build, test, package, and deploy for .NET 8 applications, including container image builds (Docker/ACR), database deployments (DACPAC/sqlpackage), and Service Bus provisioning. Author and maintain PowerShell Core 7+ modules and scripts that drive pipeline logic — configuration generation, deployment map parsing, secret retrieval, Terraform variable construction, and Helm-based deployments. Ensure pipelines enforce security, quality gates (SonarQube), and environment promotion controls (DEV → TST → VAL → Manual Gate → PRD). Standardize deployment patterns so all project teams follow consistent, auditable release workflows. Infrastructure as Code (IaC) Design, author, and maintain Terraform modules for provisioning and configuring Azure resources: AKS workloads (via Helm provider), Keycloak identity clients, SQL users, managed identities, Key Vault HSM keys, and Service Bus RBAC. Automate provisioning, scaling, and lifecycle management of infrastructure resources using Terraform, Azure CLI, and PowerShell. Manage Terraform state (Azure Storage backend with AAD auth) and ensure safe, repeatable deployments across environments. Repository & Azure DevOps Administration Administer the shared Common/devops repository and associated deployment configuration repositories. Support version control practices, branching strategy (GitFlow: DEV/MAIN/hotfix), and PR validation workflows. Manage Azure DevOps service connections, agent pools, variable groups, and environment approvals. Collaboration & Enablement Serve as the bridge between infrastructure and application development teams, helping them integrate platform capabilities into their workflows. Provide guidance and support on platform standards, deployment map configuration, pipeline extension patterns, and troubleshooting. Write and maintain documentation (README, schema references, onboarding guides) for consuming teams. Review contributions to shared platform code for architectural consistency and backward compatibility. Key Requirements - Hands-on experience operating and configuring Azure Kubernetes Service — networking, ingress, node pools, autoscaling, confidential compute, Helm chart deployments. - Strong understanding of CI/CD principles, GitFlow branching, environment promotion, and deployment automation. - Familiarity with .NET 10 build toolchain (dotnet build), NuGet, solution/project structure, and containerizing .NET applications. - Proficiency in PowerShell Core 7+ — writing modules (.psm1), classes, Pester 5+ tests, and robust scripting with proper error handling. - Broad experience with Azure services: Key Vault, Container Registry, Service Bus, Storage Accounts, SQL Database, Managed Identities, RBAC. - Understanding of cloud architecture principles — scalability, high availability, security, cost management. - Experience with Azure DevOps (TFS) — YAML pipelines, extends templates, repositories, service connections, agent pools, artifacts. - Ability to author and maintain multi-stage YAML pipeline templates with parameterization, conditions, and template composition. - Experience writing and maintaining Terraform configurations — modules, providers (azurerm, helm, keycloak, mssql, kubernetes), state management, plan/apply workflows. - Understanding of network concepts relevant to AKS and Azure — VNets, NSGs, private endpoints, DNS, ingress controllers, proxy configuration. - Ability to reason about system architecture — separation of concerns, shared platform design, module boundaries, and extensibility. Nice to Have - Experience with Keycloak or OpenID Connect identity management. - Familiarity with Docker and container image build optimization. - Knowledge of database deployment automation (DACPAC, sqlpackage). - Experience with SonarQube or similar code quality tooling. - Familiarity with confidential computing on Azure. - Experience with OpenShift container platform.

🏢 Summary: The offer is for a Software Engineer (Data) role focused on building scalable data infrastructure and production-ready ML systems within an AI-driven environment. The position combines backend engineering, data engineering, and MLOps to design high-throughput pipelines and support AI solutions from experimentation to production. The role involves close collaboration with Data Science teams to shape architecture and engineering standards for next-generation data platforms. 🗂️ Requirements: 4+ years of software engineering experience, Strong proficiency in Python, Experience building scalable production systems, Experience with data-intensive applications and SQL databases, Knowledge of data modeling and query optimization, Experience with Terraform, Kubernetes, Docker or similar tools, Understanding of CI/CD pipelines and Infrastructure as Code, Experience implementing automated testing and clean architecture principles, Ability to productionize ML solutions with Data Science teams 📃 Skills: Python, MySQL, PostgreSQL, Spark, Terraform, Kubernetes, Docker, Airflow, SQL, CI/CD, AWS, Snowflake, DBT, MLOps 🏢 Description: Our client helps small teams power big businesses with the must-have platform for intelligent marketing automation. Customers from over 170 countries depend on the Client’s mix of pre-built automation and integration to power personalized marketing, transactional emails, and one-to-one CRM interactions throughout the customer lifecycle. We’re looking for a Software Engineer (Data) to join our client’s growing AI and Data organization and help build the scalable foundations behind next-generation data and ML systems. In this role, you won’t just be working with data infrastructure; you’ll be shaping the software architecture, engineering standards, and production-ready platforms that power the company’s AI ecosystem. This is more than a traditional backend or data engineering position. You’ll work at the intersection of software engineering, data, and AI, partnering closely with Data Science and AI teams to bridge the gap between experimentation and production. From designing resilient systems to building scalable MLOps pipelines, your work will directly influence how AI solutions are developed, deployed, and scaled across the organization. Key takeaways: Stack: Python, MySQL, PostgreSQL, Spark, Terraform, Kubernetes, Docker Salary : 20 000 - 23 000 PLN gross/month, Contract of employment (+10% annual bonus, 75% Creative Tax) Working model: Hybrid, once a week in the office Location: Krakow, ul. Konopnickiej Recruitment process: Call with MOTIFE recruiter (30 min) Interview with Hiring Manager (45 min) Technical interview, live coding (1h) Cross-functional interview (1h) Responsibilities: Design, develop, and maintain scalable high-throughput data pipelines across complex data ecosystems. Build and optimize data models, schemas, and database structures to ensure long-term scalability and performance. Implement engineering best practices, including automated testing, CI/CD pipelines, and Infrastructure as Code (Terraform). Partner closely with AI and Data Science teams to productionize machine learning solutions and develop scalable MLOps pipelines. Engineer and maintain feature stores and data infrastructure supporting AI-driven initiatives. Monitor, maintain, and improve the reliability and efficiency of containerized environments using Kubernetes, Docker, and Airflow. Ensure platform stability, observability, and operational excellence through proactive system monitoring and health checks. Collaborate cross-functionally with engineering and business stakeholders to translate complex technical concepts into actionable insights. Contribute to the architectural direction and scalability of the organization’s AI and data platforms. Drive the adoption of robust software engineering standards across data and infrastructure projects. Requirements: 4+ years of experience in software engineering, with strong hands-on experience in backend development and building scalable production systems. Strong proficiency in Python and solid software engineering fundamentals, including clean architecture, testing, and maintainable code practices. Experience working with data-intensive applications, databases, and SQL, including data modeling and query optimization. Exposure to modern data engineering, cloud, or infrastructure environments, with familiarity in tools such as Terraform, Kubernetes, Docker, or similar technologies. Understanding of CI/CD pipelines, Infrastructure as Code, and general engineering best practices. Interest in AI/ML ecosystems and willingness to work closely with Data Science and AI teams on productionizing ML solutions. Familiarity with cloud platforms and modern data stack technologies such as AWS, Snowflake, Spark, or DBT is considered a strong plus. Ownership mindset and comfort working in evolving, fast-moving environments where systems and processes are still being built. What we offer: Health Benefits 1. Medical Full coverage for employees and their dependents through LUX MED. Employees have access to the “Premium” package, providing enhanced coverage and greater access to care. A client pays 100% of the premium for employees and 50% for dependents. 2. Dental No additional cost for dental coverage- integrated into LUX MED medical plan. 3. Vision Reimbursement for vision expenses up to 400 PLN every 2 years. Mental Health Tools Access to TELUS Health EAP to provide support and resources in a time of need. Additional Benefits 10% annual bonus 75% Creative Tax Vacation: 26 days. Home Office Stipend: One-time $150 equivalent home office stipend to outfit their home office. Calm Subscription: Premium subscription access to Calm, the #1 app for sleep, meditation, and relaxation. Hub Perks: Receive meal and transportation benefits when traveling to the Poland Hub. Baby Swag: If you have a baby or adopt, you’ll receive a company-branded first bath bundle. Sabbatical Program: After 5 years of employment, receive a month-long paid sabbatical leave, with a sabbatical leave bonus.

🏢 Summary: Senior Python Backend Engineer role focused on building and scaling AI-driven services and APIs for clinical trial risk detection, with strong emphasis on AI-assisted development and LLM integration. The position involves designing robust backend systems, integrating OpenAI-based capabilities, and solving performance and architectural challenges in a data-intensive SaaS environment. You will work with modern Python frameworks and cloud-native technologies in an Agile setup. 🗂️ Requirements: 6+ years of professional software development experience, Strong production-level Python experience, Experience building and consuming REST APIs, Daily professional use of AI coding assistants (e.g. GitHub Copilot), Familiarity with Specification-Driven Development (SDD), Working knowledge of BMAD methodology for AI/ML features, Experience with SQL and/or NoSQL databases, Understanding of Agile/SCRUM methodology, Knowledge of software design and architectural patterns (SOLID, Clean Architecture), Fluent English for technical collaboration 📃 Skills: Python, FastAPI, Flask, Pyramid, REST, OpenAI, GitHub, Copilot, SDD, BMAD, SQL, MySQL, MongoDB, CosmosDB, Kubernetes, Docker, Git, Celery, Temporal, Azure, CI/CD, SOLID, Agile, SCRUM 🏢 Description: About the client: Our client is a  cutting-edge, technology focused SaaS company that provides a better way of detecting and managing risks that may impact the outcome of clinical trials. Their solutions are driven by a unique set of algorithms that interrogate clinical and operational data in real-time centrally to conveniently illuminate outliers and anomalies in data. Role description You will join a 100+ strong Engineering team driving the future of data-driven statistics, machine learning, and AI software solutions. As part of a talented group of Python developers within a dynamic, fast-scaling company, you will tackle meaningful engineering challenges — and now, you will be at the forefront of integrating AI-assisted development practices into the team's everyday workflow. Our backend engineers primarily work with Python and FastAPI, broader tech stack includes: Kubernetes, Docker, Flask, Pyramid, Git, MySQL, Mongo, Azure Cosmos Db, Celery, Temporal Responsibilities: •    Design, build and maintain robust, scalable Python services and APIs •    Integrate and extend AI/LLM capabilities using the OpenAI API and related tooling •    Accelerate development velocity through effective use of GitHub Copilot and AI-assisted code review •    Apply SDD practices to ensure features are specification-aligned and well-documented from day one •    Refactor existing codebases and perform thorough peer code reviews •    Solve complex performance bottlenecks and architectural challenges at scale •    Collaborate with Product and Design to translate end-user needs into pragmatic technical solutions •    Champion engineering best practices, clean code principles and knowledge sharing within the team Requirements: •    6+ years of professional software development experience •    Strong Python expertise — production-grade services, libraries and tooling •    Proven experience building and consuming REST-based web services •    Daily use of GitHub Copilot or equivalent AI coding assistants in a professional context •    Familiarity with SDD (Specification-Driven Development) workflows •    Working knowledge of BMAD delivery methodology for AI/ML features •    Solid understanding of SQL and/or NoSQL databases •    Clear understanding of Agile/SCRUM methodology •    Strong grasp of software design and architectural patterns (SOLID, Clean Architecture, etc.) •    Confident written and spoken English for daily collaboration in an international team Nice to have: •    Containerisation — Docker, Kubernetes or equivalent orchestration platforms •    Background in clinical data, life sciences or regulated software environments •    Contributions to open-source AI/ML projects •    Experience working with Azure, understanding of CI/CD