Python & MLOps Engineer

🏢 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 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: Backend Python Engineer role focused on building a desktop control plane for deploying and configuring local AI inference on devices like NVIDIA Spark. The position involves managing model lifecycle, integrating coding agents with locally hosted models, and evolving an internal MVP toward an open-source developer tool. Work includes backend development, LLM proxy routing, Docker lifecycle management, and production-grade API design. 🗂️ Requirements: Strong Python skills, Experience with FastAPI or Flask, Knowledge of asynchronous and concurrent programming, Experience with backend API development, Understanding of Docker lifecycle management, Experience integrating external APIs, Knowledge of LLM proxy routing concepts, Ability to design idempotent endpoints and implement retry mechanisms, Experience with relational databases, Fluent English 📃 Skills: Python, FastAPI, Flask, Asyncio, LiteLLM, Pydantic, PostgreSQL, AWS, Docker, HuggingFace, CUDA, vLLM, Ollama, LLM, API 🏢 Description: VirtusLab is a leading European software consulting and engineering company, home to over 350 EU-based professionals. 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 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 We’re looking for an engineer to join a small, startup-like team building a tool for deploying and configuring local AI inference on local devices like NVIDIA Spark. You’ll work on managing the model lifecycle (start/stop) and connecting coding agents—such as Claude Code, Codex, and others—to locally hosted models. Your daily work will be on the backend: adapting LiteLLM configuration as agent APIs (Claude Code, Codex, etc.) evolve, managing the lifecycle of docker images that ship the agents, integrating with the Hugging Face API for model downloads, and getting the production essentials right—idempotent endpoints, retries with backoff, and straightforward migrations. The team already has an internal MVP and talks directly with potential users. We’re on the road to making it open source. If you want to shape a developer tool from the early stages, this is it. Project Local Inference Platform for Python Engineers Project Scope A desktop application for deploying and configuring local AI inference on local devices (e.g., NVIDIA Spark / DGX Spark), managing model lifecycle (start/stop), and connecting coding agents like Claude Code and Codex to locally hosted models. Currently an internal MVP, on the road to open source. Tech Stack Python, Asyncio, FastAPI, LiteLLM, Pydantic, PostgreSQL, AWS, Agent layer: LLM proxy routing from agents to BE Challenges Building a desktop control plane for local AI agents that abstracts away the messy parts of running LLMs on heterogeneous hardware — model lifecycle, proxy routing, agent configuration, and OS-specific quirks (path separators, signing, packaging) — while keeping the system extensible enough to evolve toward open source. Visualising model statistics and system load on top of a backend that actually does the deployment. Team Small, startup-like team on the JetBrains side — no bureaucracy, direct contact with potential users, fast iteration. Code reviews as part of the process. What we expect in general: Strong Python skills with experience in backend frameworks such as FastAPI or Flask. Solid understanding of concurrent and asynchronous programming. Ability to rapidly prototype and iterate on emerging ideas. Engineering pragmatism: cutting complexity while keeping the system extensible. System-level thinking: understanding how the Docker lifecycle, LLM proxy routing, model management, and the agent layer fit together. Hands-on experience using LLMs daily, with the judgment to know when they help and when they don’t. Self-motivation and the ability to take full ownership of features end-to-end. Fluency in English, with good communication skills for a remote-first team. Nice to have: Experience using Docker for local deployment and service orchestration. Experience setting up and maintaining an LLM proxy layer such as LiteLLM or OpenRouter. Experience with vLLM or ollama. Familiarity with the HuggingFace API and model distribution patterns. Experience with local GPU inference setups (CUDA, NVIDIA hardware) Background working with coding agents (Claude Code, Codex, Aider) and their configurations. 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: Opportunity to productionize and scale an ML-driven data quality system (Anomalsky) on GCP, combining classic ML and LLM reasoning to detect and explain row-level data anomalies. The role focuses on building anomaly detection and clustering pipelines, integrating them into acquisition workflows, and developing a real-time validation layer. You will collaborate with data engineers and product teams to operationalize models and continuously improve data quality at scale. 🗂️ Requirements: Strong Python and production ML experience, Experience deploying ML models into production pipelines, Hands-on experience with unsupervised anomaly detection (kNN, Isolation Forest, autoencoders), Experience with clustering on large-scale tabular data, Experience combining classic ML with LLMs for reasoning and validation, Experience with Airflow and Spark (Dataproc), Experience with BigQuery and Snowflake, Experience with Iceberg and Trino/Starburst, Experience with GCP and Docker, Experience with Terraform and CI tools, Experience with MLflow, Professional English proficiency 📃 Skills: Python, Airflow, Spark, Dataproc, BigQuery, Snowflake, Iceberg, Trino, Starburst, AWS, GCP, Docker, Terraform, Jenkins, GitHub, Scikit-learn, MLflow, kNN, IsolationForest, Autoencoders, Clustering, LLM 🏢 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 Productionizing and scaling an ML-driven data quality system across the organization. The scope of services involves: building and tuning anomaly-detection and clustering pipelines, pairing classic ML with LLM reasoning to flag and explain issues, collaborating with data producers to fix root causes, and creating as well as maintaining validator models that turn detected anomalies into better future data. Project Anomalsky Project Scope Our client is a NASDAQ-listed B2B data company powering Go-To-Market strategies with a 360-degree view of every customer, a view whose value depends on the quality of billions of person and company records. Anomalsky is the ML system built to catch what traditional observability misses: row-level semantic anomalies (e.g., a first_name, title, company_name). Three layers, an ML layer (embeddings + unsupervised clustering) flags suspicious records at scale, an LLM layer removes false positives and explains each cluster, and an optional human-in-the-loop lets domain experts resolve whole clusters at once. The MVP already drove ~40k crucial record corrections in production. What’s next: the MVP is landing on GCP now. Once it’s operational, the mission is to scale Anomalsky across the entire organization, embedding it into Acquisition pipelines and building a real-time variant that scans data before it reaches customers. The scope of cooperation covers Productionizing Anomalsky on GCP and scaling it to operational, organization-wide use. Evolving the ML / LLM / human-in-the-loop design and the feedback loop that turns expert reviews into reusable knowledge. Prototyping the low-latency real-time variant. Integrating Anomalsky into existing workflows, starting with Acquisition. Tech Stack Python, Airflow, BigQuery, Snowflake, Spark (Dataproc), Databricks, Iceberg, Starburst, Trino, AWS, GCP, Docker, Terraform, Jenkins, GitHub, Scikit Learn,  unsupervised anomaly detection (kNN, Isolation Forest, autoencoders), recursive clustering, classifiers on real + synthetic data, MLflow, LLM-based reasoning. Project environment ML and data engineers from VirtusLab collaborating with customer data engineers and product management. What we expect in general Strong Python and production ML skills, with a proven track record of shipping models into real production pipelines. Hands-on experience using classic ML to surface data quality issues at scale: unsupervised anomaly detection (kNN, Isolation Forest, autoencoders) and clustering on messy real-world tabular data. Practical experience pairing classic ML with LLMs: using models to flag suspicious records and LLMs for reasoning, false-positive filtering, and the final verification of anomalies. Solid data engineering background across the modern stack (Airflow, Spark/Dataproc, BigQuery, Snowflake, Iceberg/Trino) and the production toolchain (GCP, Docker, Terraform, CI, MLflow). Pragmatic, product-oriented approach focused on incremental value delivery and seamless integration into existing workflows. Professional fluency in English, enabling smooth technical and business discussions in an international environment. 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: Engineering role focused on building a desktop platform for managing and deploying local AI inference, using Kotlin and Compose Desktop. The position involves designing and implementing developer tooling such as IDE plugins and agent integrations, with full ownership over architecture, testing, and delivery. Work centers on creating extensible tooling for local LLM lifecycle management and agent connectivity. 🗂️ Requirements: Strong Kotlin or Java expertise, Experience with asynchronous and concurrent programming, Practical knowledge of Kotlin Coroutines, Experience building desktop applications, Ability to design and implement REST API integrations, Experience with configuration formats (toml, json, yaml), Knowledge of model lifecycle management, Experience with native OS packaging (msi, deb, dmg), Fluent English 📃 Skills: Kotlin, Java, Kotlinx.coroutines, Compose, Jetpack, REST, TOML, JSON, YAML, LLM, IntelliJ, Multiplatform, MSI, DEB, DMG, Swing, SWT 🏢 Description: VirtusLab is a leading European software consulting and engineering company, home to over 350 EU-based professionals. 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 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 We are looking for engineers interested in building various tooling using mainly Kotlin. Our team is working on IntelliJ plugin, IDEs, coding agents or frameworks for Local AI inference. Your job will be focused 100% on technology and your users will be developers. You will work withing, small, flexible team and you will have a lot of authonomy: you will be responsible for design, scope, testing and implementation. You will work with tooling engineers, and you will be part of an organisation specialized in developer tooling. Kotlin/Java Expert Kotlinx.coroutines / concurrent programming Advanced Compose Desktop (Jetpack Compose) Nice to have Kotlin multiplatform Nice to have Build tools/IDE development Nice to have English Advanced Project Local Inference Platform Project Scope We are building a desktop application for deploying and configuring local inference on local devices (e.g. NVIDIA Spark / DGX Spark), managing model lifecycle (start/stop), and connecting coding agents like Claude Code and Codex to locally hosted models. Currently an internal MVP, on the road to open source. Tech Stack Kotlin, Compose Desktop (with JetPack Compose roots), kotlinx.coroutines, REST APIs for model deployment management Agent layer: configuration files in toml/json/yaml, LLM proxy routing from agents to BE Packaging: native installers — msi (Windows), deb (Linux), dmg (macOS) Challenges Building a desktop control plane for local AI agents that abstracts away the messy parts of running LLMs on heterogeneous hardware — model lifecycle, proxy routing, agent configuration, and OS-specific quirks (path separators, signing, packaging) — while keeping the system extensible enough to evolve toward open source. Visualising model statistics and system load on top of a backend that actually does the deployment. Team Small, startup-like team on the JetBrains side — no bureaucracy, direct contact with potential users, fast iteration. Code reviews as part of the process. What we expect in general Strong Kotlin skills or Strong Java expertise with knowledge of Kotlin Understanding of asynchronous and concurrent programming, at best with Kotlin Coroutines High agency and ability to drive work in a changing environment Engineering pragmatism: cutting complexity while keeping the system extensible Hands-on experience using LLMs daily, with the judgment to know when they help and when they don’t Fluency in English, with good communication skills for a remote-first team Self-motivation and the ability to take full ownership of features end-to-end Nice to have Experience with different LLM agents and their configurations Experience with IDE development, especially around JetBrains IDEs Hands-on experience with developer tooling (compiler, build tools, linters, IDE, agents) Experience with creating OS-native, desktop applications Experience with creating GUIs using JVM (Swing, SWT, etc.) 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: The offer is for a Bazel-focused Developer Advocate combining consulting, development, and community engagement. The role involves leading Bazel migrations, building open source and internal tooling, and supporting pre-sales and training activities. It blends hands-on engineering with technical advocacy around build systems and SDLC practices. 🗂️ Requirements: 3+ years experience in backend, DevOps, or platform engineering, Experience with build tools and CI/CD systems, Practical experience with Bazel or another build tool, Strong knowledge of at least one ecosystem (JVM, C++, Rust, Go, NPM), Experience in developing and maintaining SDLC processes, Ability to work with C++ codebases, Experience in technical mentoring or training 📃 Skills: Bazel, Starlark, C++, CI/CD, JVM, Rust, Go, NPM 🏢 Description: VirtusLab is a leading European software consulting and engineering company, home to over 350 EU-based professionals. 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 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 Project scope This role is focused on a mixture of consult and dev-rel work around Bazel. This means a mix of:– doing Bazel migration– promoting Bazel and our expertise through conferences, meetups, blog posts, and Social Media– developing open source and internal solutions focused on Bazel– pre-sales and consultancy work– training and mentoring developers on Bazel This role usually is connected with part-time work within other projects related to Bazel. Tech stack Bazel, Starlark, C++ Challenges This position requires exceptional skills in multitasking. It requires a willingness to public speaking, write articles, and activity within Social Media or discussion forums (e.g., Bazel Slack). It requires a lot of initiative and creativity. Team 2 developer advocate/experts. What we expect in general 3+ years of experience in backend, devops, or platform engineering Some experience in developing solutions around building tools, maintaining CI/CD, and other aspects of SDLC Some experience of Bazel or at least one other build tool. Candidate should know well at least one ecosystem (JVM, C++, Rust, GoLang, NPM etc.) Communication skills and a pragmatic approach to problem-solving Ability to work as a part of a team Pro-active approach to problem-solving, without hesitation in reaching out for help Being self-driven and self-managing tasks 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 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: Engineering role focused on building and evolving SDK libraries, API definitions, and integration layers for distributed ledger financial products. The position involves advanced TypeScript development, API design, and contributing to Scala-based tooling and code generation in a distributed international environment. 🗂️ Requirements: Strong TypeScript proficiency, Experience developing reusable libraries and SDKs, Deep understanding of API design, Ability to contribute to Scala codebase, Experience with OpenAPI definitions, English level B2/C1 📃 Skills: TypeScript, Scala, Tapir, OpenAPI, SDK, API, DLT 🏢 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 This engagement is focused on architecting and delivering the developer experience and integration layer for high-stakes Distributed Ledger Technology (DLT) financial products. The project requires a specialized engineering partner capable of executing deliverables across a hybrid environment that combines expert-level TypeScript depth with the ability to competently interface with a Scala codebase. Project Scope We are working on the project together with a partner from the Smart Contracts industry. We are contributing to key components of the platform in the area of Distributed Ledger Technology (DLT). The work is divided between several teams responsible for individual components and the performance of the solution. The product is used by large international companies in the financial industry. This engagement is focused on Software Developer Kit libraries and API definitions around a wallet application that is built on top of a distributed ledger. The scope of cooperation covers SDK Evolution & Standardization: Extending and polishing the existing Wallet SDK libraries in close collaboration with product teams, while driving the design of new, multi-language client library standards with a reference implementation in TypeScript. Ledger API & Code-Gen Optimization: Refining and smoothing the Ledger API’s OpenAPI definitions using Tapir (Scala) and maintaining Scala-based code-generation tools. This autonomous role requires deep expertise in API design to deliver clean, production-ready interfaces. Tech Stack TypeScript, Scala, Tapir, OpenAPI Project Environment The initiative is driven by a Switzerland-based distributed team, interfacing directly with a Product Manager responsible for the project’s business vision. What we expect in general Strong fluency in TypeScript and its advanced design patterns. Proven experience in developing reusable libraries and SDKs. Deep understanding of APIs (from both client and server-side perspectives). Ability to competently contribute to the Scala codebase (specifically for tools and API generation). Good command of English (B2/C1). 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: Engineering role focused on building and standardizing SDK libraries and API integrations for distributed ledger financial products. The position involves advanced TypeScript development, API design, OpenAPI optimization, and contribution to Scala-based tooling in a distributed international environment. 🗂️ Requirements: Strong TypeScript proficiency, Experience developing reusable SDKs and libraries, Deep understanding of API design, Ability to work with Scala codebases, Experience with OpenAPI definitions, English level B2/C1 📃 Skills: TypeScript, Scala, Tapir, OpenAPI, SDK, API, DLT 🏢 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 This engagement is focused on architecting and delivering the developer experience and integration layer for high-stakes Distributed Ledger Technology (DLT) financial products. The project requires a specialized engineering partner capable of executing deliverables across a hybrid environment that combines expert-level TypeScript depth with the ability to competently interface with a Scala codebase. Project Scope We are working on the project together with a partner from the Smart Contracts industry. We are contributing to key components of the platform in the area of Distributed Ledger Technology (DLT). The work is divided between several teams responsible for individual components and the performance of the solution. The product is used by large international companies in the financial industry. This engagement is focused on Software Developer Kit libraries and API definitions around a wallet application that is built on top of a distributed ledger. The scope of cooperation covers SDK Evolution & Standardization: Extending and polishing the existing Wallet SDK libraries in close collaboration with product teams, while driving the design of new, multi-language client library standards with a reference implementation in TypeScript. Ledger API & Code-Gen Optimization: Refining and smoothing the Ledger API’s OpenAPI definitions using Tapir (Scala) and maintaining Scala-based code-generation tools. This autonomous role requires deep expertise in API design to deliver clean, production-ready interfaces. Tech Stack TypeScript, Scala, Tapir, OpenAPI Project Environment The initiative is driven by a Switzerland-based distributed team, interfacing directly with a Product Manager responsible for the project’s business vision. What we expect in general Strong fluency in TypeScript and its advanced design patterns. Proven experience in developing reusable libraries and SDKs. Deep understanding of APIs (from both client and server-side perspectives). Ability to competently contribute to the Scala codebase (specifically for tools and API generation). Good command of English (B2/C1). 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