Senior Python Engineer

🏢 Summary: Technical role supporting an AI platform that automates and scales ML processes, focused on maintaining and developing documentation infrastructure and API docs generation. The position involves working with OpenAPI specifications, static site generators, and CI/CD pipelines to ensure high-quality, automated documentation delivery. Includes participation in on-call rotations and collaboration within modern DevOps-driven environments. 🗂️ Requirements: Proven experience with MkDocs and MkDocs-Material, Proficiency in Python, Experience with static site generators, Experience generating API documentation from OpenAPI spec, Strong knowledge of REST API principles, Experience with Git and GitHub workflows, Experience with CI/CD pipelines and GitHub Actions, Scripting skills for task automation, Understanding of dependency management tools (e.g., Poetry), Availability for on-call duty (5 AM–5 PM CEST, once every six weeks) 📃 Skills: Python, MkDocs, Material, OpenAPI, REST, Swagger, Redoc, Git, GitHub, CI/CD, GitHubActions, Poetry, Markdown, Docker, Bash, Shell, HTML, CSS, JavaScript, WCAG 🏢 Description: We’re Devopsbay - MLOPS, DevOps and AI Specialists. We know how nodes works, how to make the cloud cheaper or adapt AI to boost any area that companies need (any many more). We support our clients with strong engineers on a project basis and are always on the lookout for stellar performers. Our clients are at the cutting edge of modern solutions. We also develop our inhouse products: https://descrb.com/ & https://defencebay.com/ Currently, we’re working with a client specialising in AI solutions at scale. The platform helps automate and scale ML processes, allowing access regardless of technical prowess. Please note that working on this project requires availability for on-call duty once every six weeks from 5 AM to 5 PM (CEST time zone). Responsibilities Support projects and smaller “paper cuts” for the docs portal Work with OpenAPI spec for REST services (portions of documentation generated from spec) Markdown, MkDocs/Material CI/CD, Git, basic shell/bash scripting Docker, k9s (work with test/deploy pipelines) Some frontend/CSS experience is a plus Required Skills and Experience Proven experience with MkDocs and MkDocs-Material. Experience with other static site generators (e.g., Hugo, Jekyll, Sphinx) is a plus. Proficiency in Python. Familiarity with dependency management tools like Poetry. Experience with scripting to automate tasks, such as managing redirects, generating documentation, and integrating with external systems. Deep understanding of REST API principles and the OpenAPI Specification. Experience with generating API documentation from spec files (e.g., using tools like Swagger or Redoc). Expertise in using Git and GitHub for version control, including pull request workflows. Experience with continuous integration/continuous deployment (CI/CD) pipelines, particularly with GitHub Actions for tasks like linting (Vale) and build processes. Knowledge of modern development best practices, including writing clean, maintainable code, and testing. Experience with web accessibility standards (e.g., WCAG) is a plus, as it's often a component of a quality front-end experience. Desirable Experience (Plus) Strong proficiency in front-end technologies: HTML, CSS, and JavaScript. Proven capability to translate design specifications into precise, pixel-accurate implementations. Understanding of basic security best practices in a development environment—especially when handling credentials, API keys, and sensitive configuration files. Awareness of potential risks in integrating AI tools (e.g., prompt injection, data exposure) is beneficial. Benefits: B2B contract International projects, often at the cutting edge of technology Experienced team - exchange your expertise with other passionate engineers Modern tech stacks Flexible hours Integration meetings

🏢 Summary: Hands-on ML Engineer role to evolve a computer vision mobile app from prototype to production, improving model accuracy and building scalable ML infrastructure. You will design automated pipelines for adding new species, optimize models for on-device deployment, and extend support to Android. The role focuses on end-to-end ML lifecycle ownership, from training to mobile deployment. 🗂️ Requirements: 5+ years of ML engineering experience, Strong experience with PyTorch, Experience with vision transformers (ViT/timm), Experience with YOLO or Ultralytics, Experience with full ML lifecycle (training, evaluation, export, deployment), Experience deploying models to CoreML or TFLite, Experience building ML pipelines with versioning and experiment tracking, Experience implementing CI/CD for ML workflows, Experience working with small or noisy datasets, Experience designing scalable classification systems, Experience with orchestration tools (Airflow, Prefect, or Dagster) 📃 Skills: Python, PyTorch, ViT, timm, YOLO, Ultralytics, CoreML, TFLite, Airflow, Prefect, Dagster, CI/CD, MediaPipe, Quantization, Pruning, Distillation 🏢 Description: We’re Devopsbay - MLOPS, DevOps and AI Specialists. We know how nodes works, how to make the cloud cheaper or adapt AI to boost any area that companies need (any many more). We support our clients with strong engineers on a project basis and are always on the lookout for stellar performers. Our clients are at the cutting edge of modern solutions. We also develop our inhouse products: https://descrb.com/ & https://defencebay.com/ Our client is building a mobile app that uses computer vision to identify fish species, classify sex, and estimate length from photos. We’ve proven the concept — species classification at 87.6% accuracy, a length regression model with 73% improvement over baseline, and CoreML export running on iOS. Now we need someone to take this from working prototype to production-grade ML system. The founder has built the initial models and pipeline in Python, and your job is to improve model accuracy, build the infrastructure to support continuous improvement, and extend deployment to Android. This is a hands-on, build-it-yourself role — not a management position. What You’ll Own Design and build a repeatable, scalable process for adding new fish species to the classifier. Today, adding a species involves manual image scraping, hand-curated CSVs, and ad hoc retraining. We need a system where a new species can go from “we want to support this” to “it’s live on device” with minimal manual effort. What We’re Looking For 5+ years of experience in ML engineering, with meaningful time spent on both model development and production infrastructure. Specifically: Strong PyTorch experience, ideally with vision transformers (ViT/timm) and object detection (YOLO/ultralytics) Familiarity with the full model lifecycle — training, evaluation, export, and on-device deployment Hands-on experience deploying models to mobile (CoreML and/or TFLite); you don’t need to be an iOS/Android app developer, but you need to produce optimized model artifacts that mobile engineers can integrate Experience building ML pipelines with proper versioning, experiment tracking, and CI/CD Comfort working with small, noisy datasets and knowing how to get the most out of limited labeled data Experience designing classification systems that scale — adding new classes without full retraining or accuracy regression Familiarity with at least one orchestration tool (Airflow, Prefect, Dagster) Self-directed — you’ll be working with a small team and need to make pragmatic architecture decisions without a lot of oversight Nice to Have Experience with MediaPipe or similar pose/landmark detection frameworks Background in ecological or biological image classification Experience with on-device A/B testing frameworks Familiarity with edge inference optimization (quantization, pruning, distillation) Experience with few-shot learning or other techniques for bootstrapping new classes with limited data

🏢 Summary: Backend Engineer role focused on building and scaling a mission-critical situational awareness platform with real-time communication and AI-driven capabilities. The position involves developing backend services, APIs, microservices, and data pipelines in a distributed, cloud-based environment. You will integrate AI technologies and external systems while contributing to system architecture and performance optimization. 🗂️ Requirements: 5+ years backend development experience, Strong proficiency in Python, Experience building scalable backend services, Solid knowledge of PostgreSQL and database design, Experience with RabbitMQ or event-driven architectures, Experience with Kubernetes, Experience with AWS, Experience designing RESTful APIs, Experience with microservices architecture, Understanding of distributed systems, Experience with asynchronous communication, Experience with real-time data processing, Experience integrating LLMs or RAG, Experience with data pipelines and ETL, Experience integrating third-party APIs, Knowledge of software architecture and system design, Ability to troubleshoot and optimize production systems 📃 Skills: Python, PostgreSQL, RabbitMQ, Kubernetes, AWS, REST, Microservices, DistributedSystems, LLM, RAG, ETL, APIs, Cloud 🏢 Description: About project: TROP is an advanced situational awareness and operational coordination platform developed by Defencebay. By combining real-time mapping, secure communications, drone integration, AI-ready workflows and resilient Mesh LoRa networking, TROP helps teams maintain a shared operational picture when standard infrastructure is limited or unavailable. We are building next-generation technology for public safety, emergency response, critical infrastructure protection and defense operations. Defencebay is a sub-brand of Devopsbay, focused on building advanced technologies for public safety, defense, and crisis communication systems. The recruitment process for this role is conducted under the Defencebay brand, as part of our dedicated product team working on the TROP platform. While being part of the broader Devopsbay organization, Defencebay operates as an independent product unit responsible for developing mission-critical solutions in the area of situational awareness and resilient communications Required Skills 5+ years of experience in backend software development. Strong proficiency in Python and experience building scalable backend services. Strong understanding of software engineering best practices, including testing, code reviews, clean code, and maintainable architecture. Solid knowledge of PostgreSQL and database design principles. Hands-on experience with message brokers such as RabbitMQ or similar event-driven architectures. Experience working with containerized environments and orchestration platforms, particularly Kubernetes (K8s). Practical knowledge of cloud platforms, preferably AWS. Experience designing, building, and maintaining RESTful APIs and microservices. Understanding of distributed systems, asynchronous communication, and real-time data processing. Experience integrating AI technologies, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), or AI-powered applications. Familiarity with data pipelines, ETL processes, and analytics workflows. Experience integrating third-party systems, APIs, and external services. Understanding of software architecture principles, system design, and scalability considerations. Ability to troubleshoot complex production issues and optimize system performance. Strong problem-solving skills and ability to work independently in a fast-paced environment. Good communication skills and ability to collaborate effectively within cross-functional teams. Nice to Have Experience working with multiple programming languages and technology stacks beyond Python. Experience with Computer Vision solutions and AI model deployment. Familiarity with Model Context Protocol (MCP) and AI agent architectures. Experience working with Team Awareness Kit (TAK), ATAK, iTAK, or situational awareness platforms. Knowledge of geospatial systems, mapping technologies, or GIS solutions. Experience in defense, public safety, emergency management, or mission-critical systems. Familiarity with DevOps practices, Infrastructure as Code (Terraform), and CI/CD pipelines. What You’ll Do Design, develop, and maintain backend services using technologies such as Python, PostgreSQL, RabbitMQ, Kubernetes (K8s), and AWS . Build and evolve APIs and microservices , with a strong focus on real-time communication features. Develop new features, maintain existing components, and troubleshoot production issues across the platform. Design and implement integrations leveraging modern AI technologies, including LLMs, RAG (Retrieval-Augmented Generation), Computer Vision, and Model Context Protocol (MCP) . Build and maintain data processing pipelines that power AI-driven features and analytics workflows. Evaluate, prototype, and integrate emerging AI technologies into real-world products and solutions. Contribute to system architecture and participate in key technical decisions shaping the platform’s evolution. Integrate with external systems and develop solutions that enable efficient communication and data exchange across distributed services. Work on products within the Team Awareness Kit (TAK) ecosystem , supporting real-time situational awareness, information sharing, and operational coordination. Take ownership of features and projects from initial concept through production deployment and ongoing operation.

🏢 Summary: The role involves designing, developing, and deploying AI and LLM-based solutions in a cloud environment, with a focus on building and enhancing Data, Analytics, MLOps, and LLMOps platforms. The position covers full lifecycle management of machine learning and language models, including deployment, monitoring, and optimization. The engineer will contribute to data architecture and distributed systems supporting AI-driven products. 🗂️ Requirements: Experience as LLM Engineer or similar AI/Data role, Around 3 years of experience with Python, Experience building Data, Analytics, MLOps or LLMOps platforms in cloud environments, Experience with Azure cloud, Knowledge of full ML model lifecycle management in cloud, Experience with Big Data open-source tools: Kafka, Airflow, Presto, Spark, Experience in database design and data modeling, Experience working with distributed systems 📃 Skills: Python, Azure, LLM, AI, MLOps, LLMOps, Kafka, Airflow, Presto, Spark, SQL, BigData 🏢 Description: Dla naszego klienta, innowacyjnej firmy z branży ubezpieczeniowej, poszukujemy : AI / LLM Engineera Zakres obowiązków: Projektowanie, rozwój i wdrażanie rozwiązań opartych o LLM oraz technologie AI w środowisku chmurowym, Tworzenie i rozwój komponentów wspierających budowę nowoczesnych platform Data / Analytics / MLOps / LLMOps, Zarządzanie cyklem życia modeli machine learningowych i rozwiązań opartych o modele językowe, w tym ich wdrażaniem, monitorowaniem i optymalizacją, Współpraca przy budowie i doskonaleniu procesów automatyzujących rozwój, testowanie i utrzymanie rozwiązań AI, Współpraca z zespołami Data, Analytics oraz IT w celu zapewnienia spójności architektury i wysokiej jakości dostarczanych rozwiązań, Udział w projektowaniu architektury danych i rozwiązań rozproszonych wspierających rozwój produktów opartych o AI. Wymagania Doświadczenie na stanowisku LLM Engineer lub w zbliżonej roli z obszaru danych i AI, Około 3 lata doświadczenia z Pythonem, Doświadczenie w tworzeniu i rozwijaniu platform z obszaru Data, Analytics, MLOps lub LLMOps w środowisku chmurowym, preferencyjnie Azure, Znajomość zagadnień związanych z pełnym cyklem życia modeli machine learningowych, w tym ich wdrażaniem, utrzymaniem oraz monitoringiem w środowisku chmurowym, Doświadczenie w pracy z narzędziami open source wykorzystywanymi w ekosystemie Big Data, takimi jak Kafka, Airflow, Presto, Spark, Doświadczenie w projektowaniu baz danych, modelowaniu danych oraz pracy z systemami rozproszonymi, Samodzielność, inicjatywa oraz umiejętność efektywnej pracy w zespole. Pracodawca oferuje: Zatrudnienie w oparciu o umowę o pracę lub B2B, Atrakcyjne wynagrodzenie, Pracę w trybie hybrydowym lub zdalnym, Elastyczne godziny rozpoczęcia pracy, Pracę w zespole rozwijającym innowacje cyfrowe w obszarze ubezpieczeń, Realną autonomię i wpływ na kierunek rozwoju platformy oraz rozwiązań biznesowych, Dostęp do nowoczesnych narzędzi i rozwiązań cloud-native, Obszerny pakiet benefitów pozapłacowych: prywatna opieka medyczna, karta sportowa, dofinansowanie do szkoleń/studiów/kursów językowych, Zniżki na produkty własne.

🏢 Summary: DevOps Engineer role focused on designing and maintaining AI-driven CI/CD pipelines and deploying machine learning models across cloud and hybrid environments. The position involves integrating AI/ML tools into DevOps processes, optimizing infrastructure for AI workloads, and ensuring security and high availability. The engineer will work with modern cloud platforms and data technologies in international, agile projects. 🗂️ Requirements: Bachelor’s degree in Engineering, IT, Science or related field, Minimum 5 years of experience in DevOps or similar role, Hands-on experience with DevOps tools in AI/ML workflows, Experience with cloud platforms and AI/ML services, Experience deploying and scaling ML models in production, Proficiency in scripting or programming languages, Experience implementing automated testing for AI workflows, Knowledge of secure environments for AI-driven systems 📃 Skills: Jenkins, GitHubActions, Kubernetes, Docker, Terraform, AWS, Azure, GCP, MLflow, Kubeflow, Airflow, Python, Bash, YAML, TensorFlow, PyTorch, Scikit-learn, Grafana, AIOps 🏢 Description: About the role As a DevOps Engineer with AI integration skills, you will collaborate across multiple implementation streams and work closely with agile teams to achieve project and client goals. We are looking for passionate engineers ready to move our projects to the next level by leveraging modern technology stacks and AI-driven solutions. You will participate in international projects based on the latest data technologies and cloud platforms. Your key responsibilities Design and maintain CI/CD pipelines incorporating AI/ML tools and frameworks. Collaborate with AI teams to deploy and scale machine learning models in production environments. Integrate AI-based monitoring, alerting, and analytics tools to improve pipeline visibility and enable predictive analysis. Optimize infrastructure resource utilization for AI applications across cloud, on-premises, and hybrid environments. Ensure strong security practices within AI-driven pipeline ecosystems. Troubleshoot pipeline and infrastructure issues, especially those involving AI components, and ensure high availability. Monitor system performance and implement improvements using AI-enhanced tools. Skills and attributes for success Hands-on experience with DevOps tools such as Jenkins, GitHub Actions, Kubernetes, Docker, and Terraform. Strong understanding of cloud platforms (AWS, Azure, Google Cloud) and their AI/ML services. Familiarity with AI workflow tools such as MLflow, Kubeflow, or Airflow. Proficiency in scripting and programming languages (Python, Bash, YAML, etc.). Ability to implement automated testing frameworks for validating AI models and workflows. Experience building secure environments for AI-driven systems. Experience integrating AI-powered monitoring tools (AIOps, Grafana with ML plugins, or custom AI diagnostics). Requirements Bachelor’s degree in Engineering, IT, Science, or a related technical field. At least 5 years of experience in a corporate IT environment in a similar role. Proven experience with DevOps tools and frameworks used in AI/ML workflows. Strong communication skills and ability to collaborate with global teams. Nice to have Certifications in DevOps or AI/ML (e.g., Kubernetes, AWS Machine Learning Specialty). Experience working with AIOps platforms. Knowledge of specialized data pipeline automation tools for machine learning. Hands-on experience with AI/ML frameworks such as TensorFlow, PyTorch, or Scikit-learn in production environments.

🏢 Summary: The offer is for a DevOps Engineer to build and operate scalable, secure AWS infrastructure for a cloud-native AI Talent Intelligence Platform. The role focuses on production-grade infrastructure, CI/CD automation, observability, security, cost optimization, and ML/AI workload support. It involves full ownership of AWS environments across development and production with strong emphasis on reliability and compliance. 🗂️ Requirements: 5+ years of production infrastructure experience, 3+ years of hands-on AWS experience, Strong experience with AWS services: RDS Postgres, VPC, IAM, ECS or EKS, ALB/NLB, Route53, S3, CloudFront, CloudWatch, Secrets Manager, Advanced Terraform usage: modules, remote state, drift detection, multi-environment setups, Experience building and maintaining CI/CD pipelines (GitHub Actions or equivalent), Proficiency with Linux and containerization, Experience implementing observability: metrics, logs, traces, alerting, On-call and incident management experience, PostgreSQL operations knowledge: backups, migrations, performance tuning, Security best practices: IAM policies, secret management, encryption, AWS cost monitoring and optimization, English proficiency Intermediate+ or higher 📃 Skills: AWS, RDS, PostgreSQL, EC2, ECS, EKS, VPC, IAM, Route53, S3, CloudFront, CloudWatch, SecretsManager, ACM, SES, Terraform, GitHubActions, Linux, Docker, Grafana, Datadog, CI/CD, ALB, NLB, TLS 🏢 Description: Andersen is hiring a DevOps Engineer in the EU for a project building cloud-native AI platforms and ensuring scalable, secure, and reliable infrastructure. The customer is an international company focused on delivering innovative digital solutions and technology-driven services. The organization supports businesses in improving operational efficiency, streamlining processes, and accelerating digital transformation initiatives. By leveraging modern technologies and industry best practices, the company develops scalable and secure solutions tailored to evolving business needs. The customer operates in a dynamic environment, emphasizing innovation, collaboration, and the continuous enhancement of products and services to create long-term value for its clients. The project is focused on developing a cloud-native Talent Intelligence Platform powered by AI and machine learning technologies. It includes building scalable AWS-based infrastructure, supporting intelligent search and ranking capabilities, and ensuring secure, reliable, and observable platform operations. Responsibilities: Owning AWS infrastructure across dev and prod — RDS (Postgres), EC2/ECS/EKS workloads, VPC, IAM, Route53, S3, CloudFront, Secrets Manager, ACM, SES, CloudWatch. Keeping dev and prod in lockstep — same Terraform modules, same patterns, real environment parity. Running CI/CD — GitHub Actions pipelines across several service repos (backend, frontend, AI/ML, gateway, billing). Trunk-based, feature-flagged, main always deployable. Codifying infrastructure — Terraform for everything that touches AWS. No click-ops. Managing observability — metrics, logs, traces, alerting. CloudWatch + Grafana/Datadog (your call, with budget). SLOs that matter. Participating in incident response — on the rotation. Writing runbooks. Leading blameless postmortems. Managing cost control — monthly bill review, right-sizing, Savings Plans / RIs, S3 lifecycle, RDS tuning. Catching anomalies before finance does. Ensuring security & compliance — IAM least-privilege, secrets rotation, vulnerability scanning, GDPR-aligned data handling with EU-resident third-party providers, backup/DR testing. Supporting ML/AI infrastructure — model serving latency budgets, embedding storage, GPU/CPU tradeoffs, A/B infrastructure for ranking experiments. Managing database operations — RDS upgrades, parameter tuning, backward-compatible migrations, read replicas, PITR. Must-have: Experience running production infrastructure for 5+ years. Experience working with AWS for 3+ years. Hands-on with: RDS Postgres, VPC networking, IAM, ECS or EKS, ALB/NLB, Route53, S3, CloudFront, CloudWatch, Secrets Manager. Terraform in anger — modules, remote state, drift detection, multi-environment. CI/CD — GitHub Actions or equivalent. Linux + containers — Docker, image hygiene, sensible base images, multi-stage builds. Observability — metrics, logs, traces, and alerting. Incident command — on-call experience, leading incidents, writing postmortems. PostgreSQL operations — backups, migrations, query performance basics, RDS quirks. Security fundamentals — IAM policy writing, secret management, TLS, encrypted-at-rest by default. Cost awareness — monitoring and optimizing AWS infrastructure costs. Level of English – from Intermediate+ and above. Nice to haves: Experience with Keycloak in production environments. Experience with Vespa, Elasticsearch, OpenSearch, or other search infrastructure. Experience with AWS Bedrock or other managed AI tooling. Experience with ML serving using SageMaker, Bedrock, or self-hosted GPU inference. Experience with GDPR compliance and EU data residency requirements. Experience with Helm, Kustomize, and ArgoCD. FinOps mindset — tagging strategy, showback, and anomaly detection. Reasons why this job would be interesting to you: Experience in teamwork with leaders in FinTech, Healthcare, Retail, Telecom, and others. Andersen cooperates with such businesses as Samsung, Siemens, Johnson & Johnson, BNP Paribas, Ryanair, Mercedes, TUI, Verivox, Allianz, T-Systems, etc.. The opportunity to change the project and/or develop expertise in an interesting business domain. Guarantee of professional, financial, and career growth! The company has introduced systems of mentoring and adaptation for each new employee. The opportunity to earn up to an additional 1,000 USD per month, depending on the level of expertise, which will be included in the annual bonus, by participating in the company's activities. Access to the corporate training portal, where the entire knowledge base of the company is collected and which is constantly updated. Bright corporate life (parties / pizza days / PlayStation / fruits / coffee / snacks / movies). Certification compensation (AWS, PMP, etc). Referral program. Private health insurance and compensation for sports activities. Your personal data is protected in accordance with GDPR regulations. Learn more: https://andersenlab.com/privacy-policy/pl Join us! https://people.andersenlab.com/

🏢 Summary: B2B contract role for a Senior DevOps Engineer working on an AI initiative to build and operate a scalable, secure end-to-end AutoML platform. The position focuses on designing cloud infrastructure, automating CI/CD for model deployment, and ensuring reliable operation of distributed AI services. You will enable production-grade ML workflows with strong emphasis on scalability, security, and automation. 🗂️ Requirements: 5+ years DevOps or Cloud Engineering experience, Hands-on experience with Docker and Kubernetes, Experience with Terraform (Infrastructure as Code), Experience with CI/CD tools, Knowledge of AWS, Azure, or GCP, Experience with secure networking and identity management, Understanding of distributed systems and scalability, Experience with monitoring, logging, and alerting tools, English proficiency, Location in Poland or EU 📃 Skills: Docker, Kubernetes, Terraform, CI/CD, GitHub, GitLab, Jenkins, AWS, Azure, GCP, Networking, IAM, Monitoring, Logging, AutoML, MLOps 🏢 Description: Holisticon Insight is a data-focused technology and consulting company, part of the Nexer Group. Our mission is simple: We make companies Data Driven. We build data platforms and data-intensive systems that solve real business problems. We work end-to-end, from integrating data from multiple sources (including IoT and operational systems), through building scalable data pipelines and semantic layers, to delivering analytics and AI solutions used in production. Our projects often combine data engineering, cloud, and software development. We don’t just process data - we also build systems that generate it, such as IoT and digital platforms, which later become part of the data ecosystem. You’ll work on real use cases – optimizing processes, enabling better decisions, and building data products used by business teams. We focus on clean architecture, modern tech stacks, and ownership of what we build. At Holisticon Insight we value strong engineering skills, autonomy, and collaboration. We keep a flat structure, small teams, and a lot of responsibility on the engineer level. Check us out! 👉 https://holisticon.pl/holisticon-insight/ We offer a B2B Contract: 155 – 185 PLN net/hour + VAT We are looking for a Senior DevOps Engineer to join a cutting-edge AI initiative of our client. The project focuses on resolving data fragmentation and incompleteness by building an end-to-end AutoML pipeline that identifies missing data, trains predictive models, and automatically enriches datasets. As a DevOps Engineer, you will ensure the platform is scalable, secure, and fully automated , enabling reliable deployment and operation of AI services in a distributed computing environment. Your responsibilities: Design and manage cloud infrastructure for a distributed AI/ML platform Transition existing scripts into a scalable, production-ready environment Build and maintain CI/CD pipelines for model deployment Automate promotion of models from staging to production Ensure environment consistency and deployment reliability Containerize AI services to guarantee repeatable and consistent execution Optimize compute costs (e.g., spot instances for training workloads) Implement monitoring, logging, and alerting systems Set up alerts for performance degradation and system anomalies Collaborate closely with Data Engineers, ML Engineers, and Fullstack Developer You might be the perfect match if you possess the following competencies: 5+ years of prior experience in DevOps / Cloud Engineering Strong hands-on experience with: Docker & Kubernetes Terraform (Infrastructure as Code) CI/CD tools (GitHub Actions, GitLab CI, Jenkins, or similar) Deep knowledge of at least one cloud platform: AWS, Azure, or GCP Experience with secure networking, identity management, and cloud architecture Solid understanding of distributed systems and scalability challenges Experience with monitoring, logging, and alerting tools Strong problem-solving skills and ownership mindset Excellent English communication skills Location in Poland or EU 🙌 Nice to have: Experience with MLOps workflows and model lifecycle management Familiarity with AutoML pipelines or AI platforms By joining Holisticon Insight you will get: Life insurance Multisport card Fully remote job Private medical care Flexible working hours B2B or contract of employment Amazing integration events on a regular basis Training budget (e.g. Microsoft Azure Certifications) Opportunity to impact our company culture build-up Work equipment (laptop, 2 monitors, and accessories)

🏢 Summary: Remote AI Lead DevOps Engineer role responsible for defining and executing the MLOps and CI/CD strategy for enterprise AI platforms. The position focuses on architecting secure, compliant, and fully automated ML lifecycle governance, ensuring auditability, reproducibility, and large-scale reliability. The role combines technical leadership with hands-on design of cloud-native, DevSecOps-driven AI infrastructure. 🗂️ Requirements: 8–10 years DevOps or Cloud Engineering experience, Minimum 3 years in technical leadership or architect role, Strong knowledge of end-to-end ML lifecycle, Expertise in CI/CD pipeline design and implementation, Advanced Infrastructure as Code experience, Experience with SAST and DAST implementation, Strong IAM and access control management in cloud, Ability to design observability frameworks for ML systems, Experience with configuration management in multi-cloud environments, Knowledge of database scaling and security, Experience implementing model governance and auditability practices 📃 Skills: MLOps, CI/CD, DevSecOps, Azure, AzureDevOps, GitHubActions, Jenkins, Terraform, CloudFormation, SAST, DAST, IAM, Ansible, Puppet, MySQL, PostgreSQL, MongoDB, Observability, Git 🏢 Description: This is a remote position. We are looking for an AI Lead DevOps Engineer to spearhead the MLOps strategy for our high-impact AI accounts. With 8–10 years of experience, you will provide the technical leadership necessary to design robust, compliant, and highly automated AI platforms. You aren't just managing pipelines; you are architect the entire lifecycle governance—ensuring reproducibility, audibility, and security at an enterprise scale. Key Responsibilities: Strategic Leadership: Provide technical direction for the DevOps squad, defining the CI/CD and MLOps roadmap for the account. Model Governance & Evaluation: Implement automated model evaluation pipelines to track accuracy, precision, and recall metrics in production. Enterprise Security: Lead the DevSecOps strategy, ensuring all AI deployments comply with enterprise security standards and global data regulations. Platform Enablement: Architect self-service platforms that allow ML engineers to deploy models with minimal friction while maintaining strict governance guardrails. Auditability & Reproducibility: Ensure that every ML experiment is fully auditable through sophisticated pipeline and dataset versioning strategies. Mentorship: Mentor senior and junior engineers, driving best practices in automation, IaC, and cloud-native architecture. Requirements 8–10 years of experience in DevOps/Cloud Engineering, with at least 3 years in a technical leadership or architect-level role. Deep understanding of the end-to-end ML lifecycle (training, validation, deployment, and retraining loops). Mastery across Azure DevOps, GitHub Actions, and Jenkins. Expert-level Terraform or CloudFormation skills, including modular architecture and cross-account cloud deployments. Significant experience implementing SAST/DAST tools and managing complex IAM/Access Control frameworks in a cloud environment. Ability to design custom observability frameworks that track model drift, pipeline failures, and infrastructure ROI. Advanced knowledge of configuration management tools like Ansible or Puppet for complex multi-cloud environments. Solid understanding of database scaling and security for MySQL, PostgreSQL, and MongoDB. Understanding of how DevOps practices support responsible AI (e.g., bias tracking and audit logs). Exceptional ability to collaborate with Architects and Data Scientists to translate high-level AI needs into operational reality. Native or C1-level English, with the ability to present technical strategies to senior stakeholders. Benefits Professional training programs Work with a team that’s recognized for its excellence. We’ve been featured in the Deloitte Technology Fast 50 & FT 1000 rankings. We’ve also received the Great Place To Work® certification for five years in a row

🏢 Summary: The offer is for an MLOps / AI Engineer to design, implement and maintain AI/ML solutions for banking and financial sector projects. The role focuses on building MLOps environments, automating training and deployment processes, and managing cloud-based AI infrastructure in production. You will support the full lifecycle of ML models, including monitoring and optimization in production environments. 🗂️ Requirements: 3–4 years of experience as MLOps Engineer, AI Engineer, Machine Learning Engineer or similar, Strong knowledge of Python, Experience building and maintaining ML solutions in production environments, Experience with Docker and containerization, Experience with AWS, Azure or Google Cloud Platform, Knowledge of CI/CD and deployment automation, Experience working with Git, Knowledge of AI/ML model monitoring in production 📃 Skills: Python, Docker, AWS, Azure, GCP, CICD, Git, MLOps, MachineLearning, Monitoring, Cloud 🏢 Description: O projekcie Poszukujemy MLOps / AI Engineera do realizacji innowacyjnych projektów dla sektora bankowego i finansowego. Osoba na tym stanowisku będzie odpowiedzialna za projektowanie, wdrażanie oraz utrzymanie rozwiązań opartych o sztuczną inteligencję, uczenie maszynowe oraz nowoczesne platformy danych. Będziesz współpracować z zespołami Data Science, Data Engineering i DevOps, wspierając cały cykl życia modeli AI – od budowy środowisk treningowych po wdrożenie modeli na środowiska produkcyjne. Twój zakres obowiązków Projektowanie i rozwój środowisk MLOps wspierających wdrażanie modeli AI/ML Automatyzacja procesów trenowania, testowania oraz wdrażania modeli Budowa i utrzymanie pipeline'ów ML oraz Data Pipelines Monitorowanie jakości, wydajności i stabilności modeli produkcyjnych Współpraca z Data Scientistami oraz zespołami developerskimi Implementacja rozwiązań opartych o Generative AI i Large Language Models (LLM) Zarządzanie infrastrukturą chmurową wspierającą rozwiązania AI Tworzenie dokumentacji technicznej oraz dobrych praktyk w zakresie AI Engineering Optymalizacja kosztów i wydajności środowisk AI/ML Nasze wymagania Minimum 3–4 lata doświadczenia na stanowisku MLOps Engineer, AI Engineer, Machine Learning Engineer lub pokrewnym Bardzo dobra znajomość Python Doświadczenie w budowie i utrzymaniu rozwiązań Machine Learning w środowiskach produkcyjnych Znajomość Docker oraz konteneryzacji aplikacji Doświadczenie z platformami chmurowymi AWS, Azure lub Google Cloud Platform Znajomość CI/CD oraz automatyzacji procesów wdrożeniowych Doświadczenie w pracy z Git Znajomość zagadnień związanych z monitoringiem modeli AI Umiejętność współpracy z zespołami Data Science i Engineering Znajomość języka angielskiego na poziomie B2–C1 Mile widziane Doświadczenie z MLflow Znajomość Apache Airflow Doświadczenie z Kubernetes Praktyczna znajomość PyTorch lub TensorFlow Doświadczenie z Large Language Models (LLM) Znajomość frameworków takich jak LangChain, LangGraph lub LlamaIndex Doświadczenie z bazami wektorowymi (Pinecone, Weaviate, ChromaDB, Qdrant) Znajomość rozwiązań RAG (Retrieval-Augmented Generation) Doświadczenie w projektach Generative AI Znajomość Apache Kafka Doświadczenie w sektorze bankowym lub finansowym Oferujemy Udział w nowoczesnych projektach związanych ze sztuczną inteligencją i uczeniem maszynowym Możliwość pracy zdalnej lub hybrydowej Elastyczne godziny pracy Stabilną współpracę przy długoterminowych projektach Możliwość rozwoju kompetencji w obszarze AI, MLOps i Generative AI Pracę z najnowszymi technologiami chmurowymi i rozwiązaniami AI Współpracę z ekspertami Data Science, AI oraz Cloud Engineering Realny wpływ na rozwój innowacyjnych produktów i usług Atrakcyjne wynagrodzenie adekwatne do doświadczenia i kompetencji Jeżeli chcesz rozwijać rozwiązania oparte na sztucznej inteligencji, wdrażać modele ML na skalę produkcyjną oraz uczestniczyć w projektach wykorzystujących Generative AI i LLM – zapraszamy do aplikowania.

🏢 Summary: DevOps Engineer role focused on global digital transformation, building and automating cloud infrastructure based on AWS and modern data architecture. The position involves designing, implementing and maintaining CI/CD pipelines and cloud solutions in an Agile/Scrum environment. 🗂️ Requirements: Minimum 5 years experience as DevOps Engineer, Strong knowledge of AWS ecosystem, Experience with CI/CD tools, Experience with Terraform, Strong knowledge of Python, Knowledge of IAM, Experience working in Agile Scrum environment, Advanced English level C1 📃 Skills: AWS, SQS, Lambda, GitHub, Actions, Terraform, Python, IAM, CICD, Agile, Scrum 🏢 Description: Wspólnie z naszym Partnerem, jednym z liderów w branży ubezpieczeniowej, poszukujemy osoby na stanowisko Devops Engineer. Projekt dotyczy transformacji cyfrowej firmy w skali globalnej, opierając się na infrastrukturze AWS oraz nowoczesnej architekturze danych. 💡 TWOJA ROLA: Projektowanie, rozwój, automatyzacja i wdrażanie infrastruktury chmurowej. Tworzenie i utrzymywanie procesów CI/CD w celu automatyzacji procesów. Dostarczanie wysokiej jakości rozwiązań. Praca w zespołach Agile/Scrum. 🔍 CZEGO OCZEKUJEMY OD CIEBIE? Minimum 5 lat doświadczenia na stanowisku Devops Engineer. Znajomość języka angielskiego na poziomie zaawansowanym (C1). Bardzo dobra znajomość ekosystemu AWS ( SQS , Lambda ). Doświadczenie w pracy z narzędziami CI/CD ( Github Actions). Doświadczenie w pracy z Terraform. Bardzo dobra znajomość Python . Znajomość IAM . ✨ OFERUJEMY: 🤖 Nowoczesny proces rekrutacji z AI Rekruterem (AIR) – podczas aplikacji możesz odbyć rozmowę z wirtualnym rekruterem 24/7, bez czekania na telefon, z natychmiastowym feedbackiem i możliwością powtórzenia rozmowy (liczy się ostatnia wersja). Finalną decyzję zawsze podejmuje Rekruter Connectis. Możliwość uczestnictwa w spotkaniach integracyjnych oraz meetupach technologicznych, umożliwiających dzielenie się wiedzą i doświadczeniem. Wsparcie dedykowanej osoby kontaktowej z Connectis, dostępnej w celu pomocy w sprawach związanych z projektem. Pracę w zespole ekspertów, w środowisku sprzyjającym wymianie wiedzy. 5000 PLN za polecenie znajomych do naszych projektów. Stabilną, długofalową współpracę w oparciu B2B. Pracę 100% zdalną. Proces zdalny. Dziękujemy za wszystkie zgłoszenia. Pragniemy poinformować że skontaktujemy się z wybranymi osobami. 12923/KD