Middle/Senior MLOps Engineer | NXJ-125

🏢 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: B2B contract role for a Data/MLOps Engineer responsible for bridging data science and production by building and maintaining scalable ML and data pipelines on AWS. The position focuses on deploying end-to-end ML lifecycles, optimizing big data processing with Spark, and implementing robust MLOps practices. The engineer will work closely with Data Scientists to productionize models and ensure reliable, high-performance cloud infrastructure. 🗂️ Requirements: Strong ML background, Experience productionizing ML models, Proficiency in Python, Strong PySpark skills, Hands-on experience with AWS SageMaker, Experience with Apache Spark, Experience building ETL/ELT pipelines, Experience with Infrastructure as Code, Experience implementing CI/CD pipelines, Knowledge of model monitoring and drift detection, Experience working in Agile environment, English level B2 minimum 📃 Skills: Python, PySpark, PyTorch, SQL, Spark, ETL, ELT, AWS, SageMaker, CDK, Lambda, Terraform, CloudFormation, CI/CD, MLOps, IaC 🏢 Description: co.brick talents — powered by AI, powered by people. Data/MLOps Engineer (CT&C Engineering) For our Client, we are looking for a Data/MLOps Engineer to join their CT&C Engineering team. In this role, you will bridge the gap between data science and production, ensuring that scalable data solutions provide efficient ingestion, transformation, storage, and real-time analysis. If you have a strong background in ML, solid PySpark skills, and know AWS SageMaker inside out, this role is for you! Quick Job Details Rate: 140 – 150 PLN/h net Form of Cooperation: B2B Contract Start Date: ASAP English: Minimum B2 level Who Our Client Is Looking For We need a technical expert who brings overall ML background knowledge and can specifically address these core needs: The Bridge to Production: You can confidently face off with Data Scientists (who often produce notebooks only) and successfully implement their work into production-quality models. ML Model Expertise: You understand different ML models, know how to monitor them, and clearly understand their pros and cons. Hands-on Implementation: You are technically capable of building and executing these solutions using PySpark and AWS SageMaker. Technical Stack Languages & Frameworks: Python, PySpark, PyTorch, SQL Data Processing: Apache Spark, ETL/ELT Cloud & Infrastructure: AWS CDK, AWS Lambdas, AWS SageMaker, Terraform / CloudFormation Methodology & Tools: Agile, CI/CD, Training Design Key Responsibilities 1. ML & Data Infrastructure Deploy and maintain end-to-end ML lifecycles (automated training, deployment, and versioning). Build and support core MLOps components like Feature Stores, experiment tracking, and model registries. Manage scalable cloud infrastructure using Infrastructure as Code (IaC) and develop robust CI/CD/CT (Continuous Training) pipelines. 2. Data Engineering & Pipeline Optimization Build high-volume ingestion and processing pipelines using Apache Spark and PySpark. Implement data models and storage optimizations for low-latency inference and high-performance analytics. Integrate automated data quality checks and observability. 3. Governance, Security & Collaboration Proactively monitor model drift, data quality, and system latency. Maintain strict versioning for data, code, and artifacts to guarantee 100% reproducibility. Operate within an Agile framework, collaborate with Data Scientists and Product Owners, and provide clear technical documentation.

🏢 Summary: Full-time Senior MLOps Engineer role focused on designing and managing Azure-based AI/ML infrastructure for global SaaS environments. The position involves building scalable cloud platforms, optimizing CI/CD pipelines, automating ML lifecycle processes, and leading enterprise AI solutions using Azure services. The role combines hands-on DevOps, cloud engineering, and AI platform development in a rapidly evolving environment. 🗂️ Requirements: Solid experience designing and managing infrastructure for AI/ML or HPC workloads, Proven experience managing and optimizing CI/CD for Azure-based SaaS applications, Hands-on experience with Azure DevOps and standard DevOps tools, Strong scripting skills in PowerShell or Bash, Experience with Terraform or similar configuration management tools, Working knowledge of version control systems, Experience in agile software development environments, Knowledge of SQL and NoSQL databases, Experience with web server configuration (IIS or Tomcat), Understanding of networking technologies (switches, routers, firewalls, VPNs), Strong understanding of security principles, Bachelor’s Degree in Computer Science or related field 📃 Skills: Azure, AzureDevOps, PowerShell, Bash, Terraform, Git, SQL, NoSQL, IIS, Tomcat, Docker, TensorFlow, PyTorch, Scikit-learn, ARM, Ansible, AzureCLI, AzureOpenAI, AzureAIFoundry, AzureDatabricks, AzureDataFactory, AzureServiceBus, AzureRedisCache, AzureMonitoring, NewRelic, Rapid7, CI/CD, YAML 🏢 Description: Svitla Systems Inc. is looking for a Senior MLOps Engineer with ML/AI for Azure for a full-time position (40 hours per week) in Poland. Our client is a leading global provider that gathers information and reports to enhance the environmental and social impacts on the business. You’ll be part of a global team supporting multiple SaaS environments that help make the world a safer place. The team uses Azure as the core, but hosts on various platforms due to specific needs and the rapid pace of development and acquisition. The environment is rapidly shifting from IaaS to PaaS to serverless, with new AI/ML components integrated. So you’ll need to apply your current skill sets and learn new things daily to push the business forward. While you’ll ultimately need a diversity of expert-level skills to succeed, having a deep understanding and experience in just a couple of areas (monitoring, scripting, automation tools, etc.) may get you in the door to a great career. Requirements: Solid experience designing, implementing, and managing infrastructure for AI/ML or HPC workloads. Proven success in managing and optimizing CI/CD for an Azure-based SaaS application. Hands-on experience with standard DevOps tools, including Azure DevOps. Expertise in scripting with at least PowerShell or Bash. Understanding of configuration management tools like Terraform. Proven experience designing, implementing, and managing infrastructure for AI/ML or HPC workloads. Working knowledge of version control/source code management tools. Experience working in an agile software development life cycle. Knowledge of databases, including SQL and NoSQL Strong knowledge of web server configuration and management, such as IIS or Tomcat. Familiarity with local and wide area networking and associated technologies, e.g., switches, routers, firewalls, VPNs. Strong understanding of security principles. Bachelor’s Degree in Computer Science or a similar area of study. Experience working in a technical, fast-paced environment with high demand and high standards. Ability to assimilate information quickly under pressure. Excellent communication and collaboration skills. Ability to communicate effectively with multiple disciplines. Excellent analytical skills with the ability to drive reliability improvements. Strong planning skills to ensure business expectations are met, and projects are delivered on time. Nice to have: Understanding of machine learning frameworks and libraries such as TensorFlow, PyTorch, or scikit-learn, and their deployment in production environments. Familiarity with cloud-based networking (VNETs, NSGs, Route Tables, Hub/Spoke-based architecture, Private Endpoints, etc.). Strong understanding of CI/CD (YAML and Classic Pipelines). Experience with PaaS (Web Apps, Function Apps, Storage Accounts, Key Vaults, Azure Redis Cache, Azure Service Bus, API Management, Azure Data Factory, Azure Databricks). Experience in monitoring (New Relic, LogEntries, Rapid7, Azure Monitoring, etc). Some experience with containers/Docker, either for a microservice-based architecture or Service Oriented, etc. Familiarity with Git, ARM, Terraform, Ansible, Azure CLI, or similar. Responsibilities: Support application infrastructure to ensure the platform is optimized for performance and reliability. Build and maintain supporting infrastructure for cloud environments and applications. Lead the design and engineering of software systems for the AI/ML Platform, contributing to the full ML development life cycle. Identify and implement opportunities to automate and streamline ML development processes, fostering efficiency and effectiveness. Building and Managing enterprise AI solutions using Azure AI Foundry & Azure's integrated platform (Azure OpenAI, AI Search, Prompt Flow), Use a wide range of Azure resources to implement scalable, reliable, and cost-efficient services. Work side by side with your engineers, guiding critical projects, using your subject matter expertise to solve complex problems. Perform and automate system administration services, including installation, configuration, maintenance, and disaster recovery Identify system-level issues related to OS configuration and virtual hardware bottlenecks. Provide automation, auditing, and other tooling for security, compliance, and resource usage. Monitor and improve processes for all deployments. Take ownership of operational excellence to establish and maintain business focused KPI's. Use these metrics not only as a basis for positive transformation, but also as a visible representation of your team's success. Maintain and take ownership of your tasks each sprint, ensuring time worked is logged, and details are added. We offer: US and EU projects based on advanced technologies. Competitive compensation based on skills and experience. Regular performance appraisals to support your growth. Flexibility in the workspace, either remote or in our welcoming office. Comprehensive medical insurance after one month. MultiSport card with access to 2500 sports facilities all over Poland Bonuses for recommendations of new employees. Bonuses for article writing, public talks, and other activities. 15 vacation days, 10 national holidays, 10 sick leaves, and family days off. Personalized learning program tailored to your interests and skill development. Free tech webinars and meetups organized by Svitla. Welcome and anniversary presents, gifts for children, and more. Regular corporate events and meetups. Awesome team, friendly and supportive community! Ref #5102

🏢 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: Remote Senior MLOps/Cloud Engineer role supporting an LLM/NLP project, focused on building and maintaining scalable ML infrastructure in the cloud. The position involves deploying, monitoring, and optimizing machine learning systems using modern DevOps and MLOps practices. Responsibilities include CI/CD implementation, container orchestration, and cloud resource optimization. 🗂️ Requirements: Experience with at least one cloud provider and related services, Experience with monitoring and logging tools, Experience with infrastructure-as-code solutions, Experience with microservices architecture, Experience with Docker and Kubernetes, Experience with ML/MLOps stack including Kubeflow and MLFlow, Experience with data versioning tools, Experience with GPU acceleration techniques, Experience with multiple database types, Experience with cloud resource optimization, Experience with CI/CD pipelines for ML models, Proficiency in Python or Bash, Experience with container orchestration platforms, Knowledge of cloud security best practices 📃 Skills: AWS, Azure, GCP, Prometheus, Grafana, ELK, Terraform, Docker, Kubernetes, Kubeflow, MLflow, Python, Bash, GPU, ECS, EKS, Swarm, CI/CD 🏢 Description: We are currently looking for a Senior MLOps/Cloud Engineer to support an LLM/NPL Project. Must-have experiences with: · One or more cloud providers and their services · Monitoring and logging tools (e.g. Prometheus, Grafana, ELK stack) · Infrastructure-as-code solutions/templates · Microservices, Docker and Kubernetes · The data science stack, including Kubeflow, MLFlow, data versioning tools, GPU acceleration techniques, and multiple types of databases · Optimizing cloud resources effectively · Implementing and maintains CI/CD pipelines for ML models · Python or Bash for automation tasks · Container orchestration platforms like Docker Swarm or Amazon ECS/EKS · Best security practices in cloud environments Location: 100% remote Language: English, German (nice to have)

🏢 Summary: Long-term B2B contract for an MLOps/LLMOps Engineer to design and build a scalable AI/LLM platform in Azure and Kubernetes environments. The role focuses on automating ML lifecycle processes, managing production-grade ML systems, and ensuring observability and compliance with AI regulations. Hybrid work model with at least one day per week onsite in Warsaw. 🗂️ Requirements: Minimum 3 years of commercial experience in DevOps, MLOps or ML production engineering, Strong knowledge of Kubernetes and Docker including cluster management and Helm charts, Experience with public cloud Azure or GCP or AWS, Experience in Python scripting, Experience in Bash or Shell scripting, Experience with ML lifecycle management tools such as MLflow or Kubeflow, Experience with Infrastructure as Code tools such as Terraform or Bicep or Ansible, Ability to provide services from Poland 📃 Skills: Azure, AzureML, AKS, Kubernetes, Docker, Helm, Python, Bash, MLflow, Kubeflow, Terraform, Bicep, Ansible, GCP, AWS, DVC, CICD 🏢 Description: MLOps / LLMOps Engineer (Azure & Kubernetes) Dla naszego klienta – lidera sektora ubezpieczeń, który rewolucjonizuje swoje usługi za pomocą AI – szukamy inżyniera gotowego wziąć na klatę budowę nowoczesnej platformy LLMOps pod najnowsze wymogi AI Act. Jeśli automatyzację stawiasz na pierwszym miejscu, a środowiska produkcyjne z modelami ML to Twój chleb powszedni – czytaj dalej! ⚡ Kluczowe informacje: Forma współpracy: Kontrakt B2B, Długofalowy Model pracy: Hybryda (Warszawa, Wola, blisko komunikacji miejskiej – min. 1 dzień w tygodniu w biurze) 🛠️ Twój codzienny stack & wyzwania: Infrastruktura AI/LLM: Projektowanie i budowa skalowalnych środowisk w oparciu o Azure Machine Learning, Azure AI Foundry oraz Kubernetes (AKS). Automatyzacja (CI/CD/CT): Wdrażanie potoków dla ML, wersjonowanie danych i modeli (DVC, MLflow) oraz Continuous Training. Observability: Monitoring modeli pod kątem Data Drift/Model Drift na produkcji. Architektura: Konteneryzacja (Docker), zarządzanie klastrami (Helm) oraz praca na styku systemów chmurowych i on-premise. 🎯 Czego oczekujemy? (Must Have): Min. 3 lata komercyjnego doświadczenia w obszarze DevOps, MLOps lub inżynierii oprogramowania z modelami ML na produkcji Bardzo dobrej znajomości Kubernetes & Docker (zarządzanie klastrami, Helm charts). Doświadczenia z chmurą publiczną (głęboki Azure lub GCP/AWS, jeśli chcesz szybko wejść w Azure). Praktyki w pisaniu skryptów w Python oraz Bash/Shell. Znajomości narzędzi do zarządzania cyklem życia modeli (np. MLflow , Kubeflow) oraz podejścia Infrastructure as Code (Terraform, Bicep lub Ansible). Świadczenia usług z terytorium Polski. ⭐ Mile widziane (Nice to Have): Doświadczenie we wdrażaniu modeli LLM i architektur RAG . Znajomość baz wektorowych (np. Azure AI Search) oraz narzędzi do monitoringu (Prometheus, Grafana). Certyfikaty: Azure DevOps Engineer Expert (AZ-400) lub Azure AI Engineer (AI-102). Oferujemy: Preferencyjne pakiety do wykupienia na Multisport i Luxmed. Współpracę w ramach kontraktu B2B. 👋 Brzmi jak Twój kolejny krok zawodowy? Aplikuj!

🏢 Summary: B2B role for an MLOps/LLMOps Engineer to design and build a scalable LLM platform on Azure and Kubernetes, ensuring automated CI/CD/CT pipelines and production-grade ML model operations. The position focuses on AI infrastructure, model lifecycle management, observability, and hybrid cloud/on-prem integration. Hybrid work model with long-term cooperation. 🗂️ Requirements: Minimum 3 years of commercial experience in DevOps, MLOps or ML engineering in production environments, Strong hands-on experience with Kubernetes and Docker, including cluster management and Helm, Experience with public cloud (Azure preferred, or GCP/AWS with readiness to work on Azure), Proficiency in Python and Bash/Shell scripting, Experience with ML lifecycle tools such as MLflow or Kubeflow, Experience with Infrastructure as Code tools (Terraform, Bicep or Ansible), Ability to provide services from Poland 📃 Skills: Azure, AzureML, AKS, Kubernetes, Docker, Helm, Python, Bash, MLflow, Kubeflow, Terraform, Bicep, Ansible, DVC, Prometheus, Grafana, GCP, AWS 🏢 Description: MLOps / LLMOps Engineer (Azure & Kubernetes) Dla naszego klienta – lidera sektora ubezpieczeń, który rewolucjonizuje swoje usługi za pomocą AI – szukamy inżyniera gotowego wziąć na klatę budowę nowoczesnej platformy LLMOps pod najnowsze wymogi AI Act. Jeśli automatyzację stawiasz na pierwszym miejscu, a środowiska produkcyjne z modelami ML to Twój chleb powszedni – czytaj dalej! ⚡ Kluczowe informacje: Forma współpracy: Kontrakt B2B, Długofalowy Model pracy: Hybryda (Warszawa, Wola, blisko komunikacji miejskiej – min. 1 dzień w tygodniu w biurze) 🛠️ Twój codzienny stack & wyzwania: Infrastruktura AI/LLM: Projektowanie i budowa skalowalnych środowisk w oparciu o Azure Machine Learning, Azure AI Foundry oraz Kubernetes (AKS). Automatyzacja (CI/CD/CT): Wdrażanie potoków dla ML, wersjonowanie danych i modeli (DVC, MLflow) oraz Continuous Training. Observability: Monitoring modeli pod kątem Data Drift/Model Drift na produkcji. Architektura: Konteneryzacja (Docker), zarządzanie klastrami (Helm) oraz praca na styku systemów chmurowych i on-premise. 🎯 Czego oczekujemy? (Must Have): Min. 3 lata komercyjnego doświadczenia w obszarze DevOps, MLOps lub inżynierii oprogramowania z modelami ML na produkcji Bardzo dobrej znajomości Kubernetes & Docker (zarządzanie klastrami, Helm charts). Doświadczenia z chmurą publiczną (głęboki Azure lub GCP/AWS, jeśli chcesz szybko wejść w Azure). Praktyki w pisaniu skryptów w Python oraz Bash/Shell. Znajomości narzędzi do zarządzania cyklem życia modeli (np. MLflow , Kubeflow) oraz podejścia Infrastructure as Code (Terraform, Bicep lub Ansible). Świadczenia usług z terytorium Polski. ⭐ Mile widziane (Nice to Have): Doświadczenie we wdrażaniu modeli LLM i architektur RAG . Znajomość baz wektorowych (np. Azure AI Search) oraz narzędzi do monitoringu (Prometheus, Grafana). Certyfikaty: Azure DevOps Engineer Expert (AZ-400) lub Azure AI Engineer (AI-102). Oferujemy: Preferencyjne pakiety do wykupienia na Multisport i Luxmed. Współpracę w ramach kontraktu B2B. 👋 Brzmi jak Twój kolejny krok zawodowy? Aplikuj!

🏢 Summary: Remote AI Senior DevOps Engineer role focused on building and automating CI/CD and MLOps pipelines to enable seamless ML model deployment from training to production. The position bridges AI development and operations, emphasizing infrastructure as code, monitoring, DevSecOps, and scalable cloud environments. It involves close collaboration with AI teams to create a self-service model deployment platform. 🗂️ Requirements: 6–8 years experience in DevOps or SRE roles, Minimum 2 years experience in MLOps or AI/ML workloads, Expertise in Jenkins, GitHub Actions, or GitLab CI/CD, Hands-on experience with Azure DevOps, Hands-on experience with Terraform, Advanced knowledge of Docker and Kubernetes, Experience setting up Prometheus and Grafana dashboards, Experience with MySQL, PostgreSQL, or MongoDB, Familiarity with Ansible, Chef, or Puppet, Experience with SAST/DAST and vulnerability scanning in CI/CD, Experience with dataset and model versioning tools, Professional English C1 level 📃 Skills: Azure, Terraform, CloudFormation, Jenkins, GitHub, GitLab, Docker, Kubernetes, Prometheus, Grafana, MySQL, PostgreSQL, MongoDB, Ansible, Chef, Puppet, SAST, DAST, DVC, CI/CD, MLOps 🏢 Description: This is a remote position. Virtusa is seeking an AI Senior DevOps Engineer to bridge the gap between AI development and production-grade operations. You will be responsible for building the automated "highways" that allow ML models to flow from training to deployment seamlessly. This role requires a strong DevOps foundation combined with an understanding of the unique challenges of MLOps, such as GPU resource management, model versioning, and performance monitoring. Key Responsibilities: CI/CD & MLOps Pipelines: Build and maintain automated pipelines for ML models using Azure DevOps, GitHub Actions, or Jenkins. Workflow Automation: Automate model validation, packaging, and deployment workflows to ensure rapid iteration cycles. Infrastructure as Code (IaC): Use Terraform or CloudFormation to provision and manage cloud-native infrastructure, focusing on high-availability and scalability. Monitoring & Observability: Set up comprehensive monitoring for infrastructure (CPU/GPU/Memory) and model performance (latency and drift) using Prometheus and Grafana. DevSecOps Implementation: Integrate security into the heart of the pipeline, including secret management, IAM role configuration, and vulnerability scanning. Collaboration: Work closely with Data Scientists and AI Engineers to enable a self-service platform for model deployment. Requirements 6–8 years of experience in DevOps/SRE roles, with a minimum of 2 years focused on MLOps or supporting AI/ML workloads. Deep expertise in Jenkins, GitHub Actions, or GitLab CI/CD. Hands-on proficiency with Azure DevOps and Terraform (CloudFormation is a strong plus). Advanced knowledge of Docker and Kubernetes for managing distributed AI applications. Proven experience setting up Prometheus and Grafana dashboards for technical and model-specific metrics. Practical experience managing or connecting to MySQL, PostgreSQL, or MongoDB. Familiarity with Ansible, Chef, or Puppet for automated environment setup. Hands-on experience with SAST/DAST tools and automated vulnerability scanning within CI/CD pipelines. Experience with versioning tools for datasets and models (e.g., DVC or similar pipeline versioning logic). Professional English (C1) for seamless interaction with global delivery teams. 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: 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: The role focuses on building and operating scalable, low-latency machine learning infrastructure for real-time personalization services. You will design and maintain Python-based microservices, deploy ML models to cloud environments, and ensure high availability and performance of recommendation systems. The position involves close collaboration with ML and platform teams to bring ML-powered features into production. 🗂️ Requirements: 3+ years of professional software engineering experience, Degree in Computer Science, Engineering, or related technical field, Strong knowledge of data structures and algorithms, Professional experience building backend services in Python, Experience designing RESTful APIs or gRPC services, Experience building microservices architectures, Experience deploying production services on AWS, GCP, or Azure, Experience with relational and non-relational databases, Experience with event-driven architectures and message queues, Strong debugging, profiling, and performance tuning skills 📃 Skills: Python, AWS, GCP, Azure, Kubernetes, Docker, REST, gRPC, Kafka, RabbitMQ, SQS, Postgres, MySQL, DynamoDB, Redis, CI/CD, Terraform, Datadog, Grafana, MLflow 🏢 Description: We are hiring on behalf of our client, a global innovator in fitness and wellness technology. Their mission is to empower people to live fit, strong, long, and happy lives by delivering integrated experiences to millions of members anytime, anywhere. We are looking for a Machine Learning Infrastructure Engineer to join the Personalization team, which owns the systems powering content recommendations across the company’s digital ecosystem. In this role, you will design, build, and maintain low-latency, highly scalable services that make real-time personalization possible. You will work hands-on with backend services, cloud infrastructure, model serving, observability, and performance optimization, partnering closely with ML Engineers, API Engineers, Platform Engineers, and Product Managers to bring ML-powered product features into production. Key takeaways: Stack : Python, AWS/GCP/Azure, Kubernetes, Docker, REST, gRPC, Kafka/RabbitMQ/SQS, Postgres, MySQL, DynamoDB, Redis, CI/CD, Terraform, Datadog/Grafana/MLflow Salary : 24 000 PLN - 28 000 PLN gross on the Contract of Employment Working model : Hybrid - 3 days/week from the office Location : ul. Grzybowska 60, Warsaw Recruitment process : A call with MOTIFE Recruiter Hiring Manager screening Coding interview Panel interviews with the team (coding, architecture, and cross-collaboration interviews, Hiring Manager meeting) Responsibilities: Design, build, and maintain Python microservices powering personalized content recommendations. Productionize, deploy, monitor, and operate machine learning services in cloud-based production environments. Partner with ML Engineers to integrate models into scalable backend services and real-time recommendation workflows. Ensure high availability, low latency, and strong performance through caching, load balancing, auto-scaling, and capacity planning. Own and improve personalization services, including reliability, testability, observability, scalability, and operational readiness. Conduct performance tuning, profiling, and latency optimization for high-traffic recommendation workloads. Collaborate with platform teams to use infrastructure, tooling, and deployment workflows that support fast product iteration. Work with Product Managers, ML Engineers, API Engineers, and Data Engineers to launch ML-powered personalization features. Requirements: 3+ years of professional software engineering experience and a degree in Computer Science, Engineering, or a related technical field. Strong software engineering fundamentals, including data structures, algorithms, clean code, testing, and reproducibility. Professional experience building backend services in Python; experience with Java, Kotlin, Go, C, or C++ is welcome. Experience designing and building RESTful APIs, gRPC services, or microservices from the ground up. Strong experience deploying and managing production services on AWS or GCP, or Azure. Experience with relational and non-relational databases such as Postgres, MySQL, DynamoDB, or Redis. Experience with event-driven architectures and message queues such as Kafka, RabbitMQ, or SQS. Strong debugging, profiling, and performance tuning skills, including latency tracking, scalability analysis, and production troubleshooting. What we offer: 100% paid medical care Multisport Creative tax (KUP) Home office allowance MacBook Pro Apply now If this sounds like your next step, we’d love to hear from you! Please apply via our careers page and submit your CV in English.