AI Lead DevOps Engineer

🏢 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: Remote AI Lead Data Engineer role responsible for designing and delivering enterprise-grade AI data platforms while leading a team of engineers. The position focuses on building scalable Lakehouse architectures, advanced MLOps frameworks, and LLM orchestration pipelines. It bridges Data Science and Data Engineering to ensure robust, governed, and production-ready AI infrastructure. 🗂️ Requirements: 8–10 years of Data Engineering experience, Minimum 3 years in technical lead role, Expert-level Python, Expert-level SQL, Expert-level PySpark in distributed environments, Experience with SageMaker, Azure ML, or Vertex AI, Hands-on MLflow or Kubeflow for CI/CD, Experience with Monte Carlo or Datadog, Experience with Collibra or Alation, Implementation of Responsible AI guardrails, Knowledge of Medallion, Data Mesh, Lakehouse architectures, Experience with PII masking and data lineage, Knowledge of HIPAA and GDPR compliance 📃 Skills: Python, SQL, PySpark, SageMaker, AzureML, VertexAI, MLflow, Kubeflow, MonteCarlo, Datadog, Collibra, Alation, Lakehouse, Medallion, DataMesh, CI/CD, LLM, HIPAA, GDPR 🏢 Description: This is a remote position. The AI Lead Data Engineer acts as the technical lighthouse for our data squads. With 8–10 years of experience, you are responsible for the technical design and delivery of robust AI data platforms. You will bridge the gap between Data Science and Data Engineering, ensuring that our infrastructure supports advanced MLOps and LLM requirements while leading a team of engineers to maintain elite coding and governance standards. Key Responsibilities: Technical Leadership: Lead a squad of data engineers in the design and execution of end-to-end AI data architectures. AI Observability & Governance: Build frameworks for bias detection, ethical AI considerations, and auditability using platforms like Collibra or Alation. Infrastructure Design: Lead the transition to Data Lakehouse architectures and implement feature stores for enterprise-wide model reuse. Delivery Management: Work with stakeholders to manage project milestones, technical risks, and on-time delivery. Advanced MLOps: Implement enterprise-grade CI/CD for ML workflows using MLflow or Kubeflow. GenAI Orchestration: Design specialized pipelines for LLM evaluation frameworks and prompt-tuning datasets. Requirements 8–10 years of experience with at least 3 years in a lead role managing technical delivery. Expert-level Python, SQL, and PySpark optimization for distributed environments. Deep experience with AI platform services such as Amazon SageMaker, Azure ML, or Vertex AI. Hands-on experience implementing enterprise observability with Monte Carlo or Datadog. Experience with data governance platforms (Collibra/Alation) and implementing Responsible AI guardrails. Deep understanding of modern data patterns (Medallion architecture, Data Mesh, Lakehouse). Advanced knowledge of security frameworks, including PII masking, data lineage, and HIPAA/GDPR compliance. Exceptional ability to mentor junior engineers and communicate complex data strategies to business 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: 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: Technical leadership role combining hands-on DevOps/SRE engineering with team management to build and operate large-scale GPU infrastructure for AI workloads. Focused on infrastructure automation, reliability, observability, and high-performance networking across complex production environments. Responsible for shaping IaC standards, CI/CD, and operational excellence for software-defined, GPU-based platforms. 🗂️ Requirements: 8+ years in DevOps, SRE, or Platform Engineering, 3+ years in technical leadership role, Experience with large-scale infrastructure automation, Proficiency in Infrastructure as Code tools, Experience with GitOps and CI/CD, Hands-on experience with Kubernetes, Experience with GPU technologies, Scripting or programming in Python, Go, or Bash, Experience with bare-metal provisioning, Knowledge of observability and monitoring tools, Understanding of distributed systems reliability, Experience with high-performance networking technologies, Ability to lead technical discussions and mentor engineers, English proficiency at communicative level 📃 Skills: Terraform, Ansible, Pulumi, Crossplane, GitOps, Kubernetes, NVIDIA, MIG, Python, Go, Bash, Prometheus, Grafana, Loki, OpenTelemetry, RDMA, InfiniBand, RoCE, CI/CD 🏢 Description: About the role We are looking for a Lead DevOps Engineer to provide technical leadership for DevOps and Site Reliability Engineering practices supporting large-scale GPU infrastructure used for AI training and inference workloads. This role combines hands-on engineering with team leadership. You will be responsible for shaping automation standards, improving platform reliability, and leading a team working on software-defined infrastructure, high-performance networking, observability, and operational excellence across complex production environments. Responsibilities Lead, mentor, and support a team of DevOps and SRE engineers working across the full lifecycle of GPU infrastructure platforms Design and implement Infrastructure as Code solutions for provisioning and managing bare-metal GPU servers, networking, storage, and cluster orchestration components Build and improve CI/CD pipelines for infrastructure, platform services, and internal tooling Develop and maintain monitoring, logging, alerting, and observability solutions for large-scale GPU environments Define and track SLIs/SLOs , improve incident response processes, and contribute to post-incident reviews and long-term reliability improvements Work closely with Infrastructure, Networking, Facilities, and AI/ML teams to ensure stable and scalable platform operations Automate operational processes such as cluster scaling, firmware and BIOS updates, hardware diagnostics, and capacity planning Support DevSecOps practices, including infrastructure hardening, vulnerability management, and compliance automation Identify operational inefficiencies and reduce repetitive manual work through automation Evaluate and introduce new tools and solutions related to GPU infrastructure, orchestration, and cloud-native operations Requirements 8+ years of experience in DevOps, SRE, Platform Engineering , or a similar area At least 3 years of experience in a technical lead, lead engineer, or team leadership role Strong practical experience with infrastructure automation in large-scale or complex production environments Very good knowledge of Terraform, Ansible, Pulumi, Crossplane , or similar Infrastructure as Code tools Experience with GitOps , configuration management, and CI/CD practices Hands-on experience with Kubernetes Experience working with GPU-related technologies such as NVIDIA GPU Operator, device plugins, MIG, or time-slicing Good scripting or programming skills in Python, Go, or Bash Experience with bare-metal provisioning, infrastructure automation, or data center environments Good knowledge of observability tools such as Prometheus, Grafana, Loki, and OpenTelemetry Good understanding of distributed systems reliability and production incident management Experience with high-performance networking technologies such as RDMA, InfiniBand, or RoCE will be a strong advantage Ability to lead technical discussions, support team development, and communicate effectively with both technical and business stakeholders English proficiency at least at a communicative level is required, as you will be working in an international team Nice to have Experience in AI infrastructure, HPC environments, hyperscale infrastructure, or data center operations Familiarity with orchestration and scheduling tools such as Slurm, Ray, Run:ai, KServe , or Kubernetes-based schedulers Experience integrating telemetry from power, cooling, or environmental systems Experience building internal platforms or self-service tools for engineering or research teams Understanding of security, compliance, and audit requirements in regulated or security-sensitive environments What we offer Benefits package Opportunity to shape the DevOps and SRE foundation for advanced GPU infrastructure supporting AI workloads Real impact on the scalability, reliability, and operational standards of next-generation compute environments Collaboration with experienced engineers across infrastructure, platform, and AI domains A dynamic environment with space for ownership, technical leadership, and professional growth

🏢 Summary: Lead AI Software Developer role focused on architecting and scaling AI-powered applications, backend systems, and multi-agent workflows across cloud and enterprise environments. The position involves designing secure integrations, optimizing LLM-based solutions, and leading technical delivery and best practices. It combines hands-on AI development with backend architecture and technical leadership. 🗂️ Requirements: Degree in Computer Science, AI, Data Science or related field, 8+ years of software engineering experience, 5+ years of Python backend development in production, Experience building and scaling Python applications, Hands-on experience with AI Agents, RAG and MCP, Experience with AWS or Azure cloud platforms, Experience with storage systems and databases, Knowledge of containerization and Kubernetes, Experience with API integrations and enterprise system connectivity, Leadership and mentoring experience 📃 Skills: Python, AWS, Azure, Kubernetes, RAG, MCP, APIs, Cloud, Databases, Containerization, AI, LLM 🏢 Description: We are seeking an experienced Lead AI Software Developer to design and scale AI-powered applications and integrations. In this role, you will architect backend systems, lead AI agent development, and ensure secure, scalable integration across cloud platforms and enterprise systems. Key Responsibilities Design and maintain middleware, APIs, and connectors integrating AI agents with enterprise systems Architect scalable backend services and cloud-based solutions (AWS/Azure) Develop and optimize multi-agent workflows and adaptive decision logic Fine-tune prompts and LLM-based solutions for domain-specific use cases Lead code reviews, mentor developers, and define development best practices Drive complex API integrations with internal and external AI platforms (incl. MCP servers) Remove technical roadblocks and ensure timely delivery Collaborate with AI, Data, and Platform teams to optimize automation architectures Maintain technical documentation and promote continuous improvement Requirements Degree in Computer Science, AI, Data Science, or similar field 8+ years of software engineering experience, including 5+ years of Python backend development (production) Strong experience building and scaling Python applications Hands-on experience with AI technologies (AI Agents, RAG, MCP) Experience with cloud platforms (AWS or Azure), storage, databases, and AI services Knowledge of containerization and Kubernetes Experience with API integrations and enterprise system connectivity Strong leadership and mentoring experience Excellent communication skills; fluent in Polish and English (min. B2); German is a plus Willingness to travel internationally (~10%) Nice to have: Experience with LangChain, LangGraph, multi-agent systems, prompt engineering, and LLM fine-tuning. What We Offer Work in a global and innovative technology environment Direct impact on enterprise AI and automation solutions Professional development and access to training programs Hybrid work model with flexible hours (central Wroclaw office) Competitive benefits package, cafeteria system, CSR initiatives, and employee clubs

🏢 Summary: Lead Data Engineer role focused on driving AI initiatives and building scalable cloud-based data architectures in a global environment. The position involves technical ownership of data platforms, designing secure and high-performing systems, and leading data engineering efforts in a DevOps setting. The role emphasizes AI integration, cloud infrastructure, and enterprise-grade data governance. 🗂️ Requirements: Degree in Computer Science, AI, Data Science, Software Engineering or equivalent experience, 8+ years in software engineering, 5+ years of backend development with Python in production, Strong experience designing and scaling complex data systems, Hands-on experience with AI technologies, Hands-on experience with AWS or Azure, Strong knowledge of Python and SQL, Experience with APIs and data integration, Experience with automation tools, Knowledge of data governance practices, Understanding of data security and compliance standards, Proven experience leading and mentoring engineers 📃 Skills: Python, SQL, AWS, Azure, Java, AI, RAG, MCP, APIs, DevOps, Automation, Monitoring, Cloud, DataEngineering, DataPipelines, Governance, Security, Compliance, Backend 🏢 Description: We are looking for an experienced Lead Data Engineer to drive AI and cloud-based data solutions within a global technology organization. In this role, you will lead data initiatives, shape scalable architectures, and collaborate with both technical teams and senior stakeholders to deliver secure and high-performing systems. Key Responsibilities: Act as the main point of contact for data access and system-related topics with senior stakeholders Lead and mentor data engineers, promoting best practices and technical excellence Design, build, and maintain scalable cloud infrastructure and data pipelines Ensure data quality, security, compliance, and governance across the full lifecycle Develop secure and reliable cloud architectures (AWS/Azure) for AI and enterprise applications Implement monitoring, alerting, disaster recovery, and business continuity solutions Take technical ownership of applications within a DevOps environment Drive automation and self-service capabilities Support AI initiatives (e.g., AI Agents, RAG, MCP) with focus on quality and scalability Stay updated on emerging technologies and advise on strategic data direction Requirements: Degree in Computer Science, AI, Data Science, Software Engineering, or equivalent experience 8+ years in software engineering, including 5+ years of backend development with Python (production level) Strong experience designing and scaling complex data systems Hands-on experience with AI technologies and cloud platforms (AWS or Azure) Solid knowledge of Python, SQL (Java is a plus) Experience with APIs, data integration, automation tools, and data governance Strong understanding of data security and compliance standards Proven leadership and mentoring experience Excellent communication skills in English and Polish (min. B2); German is a plus What We Offer: Opportunity to work in a global, international environment Real impact on AI and cloud solutions in a large-scale organization Access to training platforms and professional development programs Hybrid work model with flexible hours (modern office in central Wroclaw) Comprehensive benefits package (medical & dental care, sports card, life insurance, mental health program) Cafeteria benefits platform with monthly points CSR initiatives, integration events, and employee passion clubs

🏢 Summary: Lead AI Forward Deployment Engineer role focused on deploying, securing, and scaling AI solutions in enterprise environments using Google Cloud. The position bridges AI R&D and production by architecting Vertex AI pipelines, managing GKE clusters, and integrating AI into existing systems. It involves leading technical deployments and ensuring robust security for large-scale AI workloads. 🗂️ Requirements: 10+ years experience in Distributed Systems, Data Engineering, or DevOps, Expert-level knowledge of GCP and Vertex AI, Proven experience with GKE, Helm, and Istio, Strong Python experience with asynchronous and scalable backend development, Hands-on experience securing endpoints and AI systems, Experience designing end-to-end AI pipelines, Ability to manage GPU/TPU workloads, Legal work permit in Poland 📃 Skills: GCP, VertexAI, GKE, Kubernetes, Helm, Istio, Python, APIs, Microservices, DistributedSystems, DevOps, FeatureStore, Pipelines, ModelGarden, CloudArmor, ModelArmor, WAF, GPU, TPU, Networking, VPC 🏢 Description: Project info: We are seeking a skilled Lead AI Forward Deployment Engineer (GCP) to join our client's team. As a “Special Ops Engineer”, you will bridge the gap between our AI R&D and enterprise production. You will deploy, secure, and scale AI solutions within complex client environments using the Google Cloud ecosystem. Responsibilities: Production AI: Architect end-to-end pipelines on Vertex AI (Model Garden, Pipelines, and Feature Store). Security Hardening: Implement Model Armor for LLM safety (prompt injection/PII filtering) and Cloud Armor for edge-layer WAF protection. Orchestration: Design and manage high-scale Google Kubernetes Engine (GKE) clusters, optimizing for GPU/TPU workloads. Integration: Write production-grade Python microservices and APIs to embed AI into existing enterprise workflows. Client Leadership: Lead technical deployments on-site or in-client VPCs, navigating complex networking and legacy constraints. Job requirements: 10+ Years Engineering: Deep background in Distributed Systems, Data Engineering, or DevOps. GCP Mastery: Expert-level knowledge of Vertex AI and the broader GCP ecosystem. K8s Expert: Proven experience with GKE, Helm, and Service Mesh (Istio). Python Veteran: Master of asynchronous programming and scalable backend design. Security Expert: Hands-on experience securing endpoints and managing AI-specific vulnerabilities. Must possess a legal work permit in Poland Benefits: General benefits - depends on the form of employment Hybrid work model & remote work Attractively located office with collaboration spaces Onsite parking space for employees Referral program with financial bonus Life Insurance Budget for development (including language courses and others), clear career path with the possibility to gain experience in international environment Access to internal Learning Platform with multiple trainings oriented for professional growth Lifestyle benefits: Access to MyBenefit platform (Multisport included) Team Building activities Charity initiatives Working environment promoting diversity and inclusion Health benefits: Private medical care - Platinum Package

🏢 Summary: The offer is for an AI DevOps Lead Engineer responsible for designing and managing cloud infrastructure on Azure and GCP with a strong focus on Infrastructure as Code and CI/CD automation. The role involves leading infrastructure initiatives, administering Kubernetes clusters, and implementing secure, scalable, and resilient cloud solutions. It is a long-term B2B contract with a hybrid work model in Cracow. 🗂️ Requirements: 4+ years experience in DevOps, SRE, or Cloud Engineering, Strong knowledge of Linux/UNIX systems, Production experience with GCP and/or Azure, Experience managing GKE and/or AKS clusters, Proficiency in Infrastructure as Code with Terraform, Experience building and maintaining CI/CD pipelines with Jenkins, Scripting skills in Python and/or Bash 📃 Skills: GCP, Azure, GKE, AKS, Terraform, Jenkins, Python, Bash, Linux, UNIX, CI/CD, IaC 🏢 Description: We are seeking an accomplished AI DevOps Lead Engineer with a strong foundation in cloud infrastructure management and DevOps practices. The ideal candidate will have over 4 years of experience in DevOps, SRE, or Cloud Engineering roles, with a proven record of designing and implementing robust cloud solutions, particularly on GCP and Azure platforms. Strong expertise in Infrastructure as Code (IaC) and CI/CD pipeline management is imperative, along with scripting capabilities in Python and Bash. What we offer: Long Term B2B Contract Rate: 200 PLN/ H +VAT Hybrid Cracow ( 1-2 times per week) Main Responsibilities: Design, build, and manage core infrastructure on Azure using IaC principles. Administer and enhance GKE and AKS clusters, ensuring security, scalability, and resilience. Evolve Jenkins pipelines and developer tooling for improved automation and efficiency. Implement security controls and automate vulnerabilities remediation. Develop a robust monitoring and alerting framework for operational visibility. Implement security gateways for improved governance. Key Requirements: 4+ years of experience in a DevOps, SRE, or Cloud Engineering role. Deep knowledge of Linux/UNIX operating systems. Production experience with GCP and/or Azure, including management of clusters (GKE, AKS). Strong proficiency with Infrastructure as Code using Terraform. Proven experience with CI/CD pipeline development using Jenkins. Scripting and automation skills in Python and/or Bash. Nice to Have: Experience with monitoring tools like Prometheus, Grafana, and Loki. Familiarity with container management using Docker. Knowledge of additional cloud services and platforms.

🏢 Summary: Senior DevOps Engineer role focused on AI and platform operations, ensuring production stability, CI/CD pipeline management, and Infrastructure as Code implementation. The position involves supporting AI/ML workloads, enhancing observability, and driving automation and self-healing infrastructure solutions. The role combines operational leadership with hands-on DevOps and MLOps practices in complex production environments. 🗂️ Requirements: Hands-on experience with Docker and Kubernetes, Experience with CI/CD tools including Jenkins and Maven, Experience with Infrastructure as Code using Ansible, Experience with relational databases such as Oracle and DB2, Knowledge of Spring, Hibernate, XML, JSON, Kafka, Advanced troubleshooting using Splunk, Apica, Sysdig, Experience with Prometheus, Grafana, ELK stack, Experience with MLOps and AI/ML workload deployments, Knowledge of AI model serving and ML containerization, Understanding of AI agent orchestration and workflow automation 📃 Skills: Docker, Kubernetes, Jenkins, Maven, Ansible, Oracle, DB2, Spring, Hibernate, XML, JSON, Kafka, Splunk, Apica, Sysdig, Prometheus, Grafana, ELK, MLOps, AI, ML 🏢 Description: As a recruitment company, DCG understands that every business is powered by experienced professionals. Our management style and partnership approach enable us to meet your needs and provide continuous support. Due to our ongoing growth and the large number of recruitment projects we undertake for our partners, we are currently looking for: Senior DevOps Engineer (AI & Platform Operations) Responsibilities: Incident & Problem Management: Lead RCA processes and implement preventive measures Production Support: Monitor, analyze, and optimize service performance and stability CI/CD Management: Build, maintain, and enhance deployment pipelines for multiple APIs Environment Management: Maintain Pre-Production and Production environments Cross-team Collaboration: Work closely with Platform Operations teams and delivery factory Documentation: Build comprehensive runbooks and knowledge base for operational procedures Innovation Leadership: Evaluate and implement new DevOps tools, practices, and AI-powered solutions Implement Infrastructure as Code practices Drive automation and self-healing infrastructure solutions Support AI/ML workload deployments and operations Lead adoption of emerging technologies and best practices Requirements: Hands‑on experience with Docker containerization and Kubernetes orchestration Experience working with CI/CD tools, including Jenkins and Maven Experience with Infrastructure as Code using Ansible Experience working with relational databases such as Oracle and DB2 Basic knowledge of application technologies including Spring, Hibernate, XML, JSON, and Kafka Advanced troubleshooting experience using Splunk, Apica, and Sysdig Experience with modern observability tools, such as Prometheus, Grafana, and preferably the ELK stack Experience with AI/ML operations (MLOps) and AI agent deployments Knowledge of AI model serving platforms and ML workload containerization Understanding of AI agent orchestration and workflow automation Experience implementing innovative solutions using emerging technologies Ability to evaluate and integrate new DevOps tools and practices Very good command of English, both spoken and written Nice to have: Software development knowledge in Java / J2EE Experience working with cloud platforms, such as AWS, Azure, or GCP Experience with IBM DataStage Offer: Private medical care Co-financing for the sports card Constant support of dedicated consultant Employee referral program

🏢 Summary: The role involves owning the full MLOps lifecycle for an AI-powered sports highlights platform, from model research and training to production deployment and scalable cloud infrastructure. You will build and maintain ML pipelines, automate workflows across Computer Vision and NLP, and ensure reliable CI/CD processes in a high-uptime environment. 🗂️ Requirements: 4+ years experience in MLOps, DevOps, ML Engineering or similar role, Experience in large-scale, high-uptime production cloud environments, Strong knowledge of Linux, Experience with Docker, Experience with Kubernetes, Experience building CI/CD pipelines for distributed systems, Proficiency in Python scripting, Experience with Terraform, Understanding of machine learning fundamentals and frameworks 📃 Skills: Python, Linux, Docker, Kubernetes, Terraform, CI/CD, ClearML, W&B, SageMaker, Azure, ComputerVision, NLP, MLOps 🏢 Description: We’re seeking a skilled and motivated MLOps Engineer to join a fast-moving team building an AI-powered platform for automatic sports highlights generation. In this role, you’ll take ownership of the full MLOps lifecycle—from research and model training to production deployment and scalable infrastructure. You’ll design and maintain robust ML pipelines, automate workflows across Computer Vision, NLP, and Data Science, and build reliable CI/CD processes that keep high-uptime systems running smoothly. Your work will help deliver cutting-edge AI experiences at cloud scale, powering innovation and performance across the entire platform. Responsibilities Own and manage every aspect in the MLOps life cycle for our AI-based automatic sports highlights generation platform Design, implement and maintain our whole ML infrastructure, from research to production and from model training to data engineering Automate and innovate workflows such as serving and training pipelines for multidisciplinary ML algorithms including Computer Vision, NLP and Data Science Build and maintain CI/CD pipelines, releases and Source Code workflows Requirements 4+ years of experience as an MLOps engineer, DevOps engineer, ML Engineer, or in a similar field Experience in a large, complex, large-scale, high-uptime production Cloud environment Core understanding of Linux OS, Docker components, and Kubernetes Experience with CI/CD pipelines for distributed production systems Experience with Python scripting Work experience with Terraform Working with MLOps platforms such as Experiment Tracking, Model Registry, feature Store - an advantage (e.g ClearML , W&B, Aws Sagemaker) Highly motivated, goal-driven, innovative, curious, and open-minded Will be a plus Working with Azure Cloud in a high-scale production environment Understanding of AI and machine learning fundamentals, concepts and frameworks What we offer Competitive salary range Medical insurance Paid vacation and sick leaves MultiSport card Top equipment kit, co-workings Hybrid set of works (Office location: Warsaw) Collaborative and innovative work environment Career growth and development opportunities A chance to work with giants of the sports industry About the project Our partner leads the industry in generating dynamic sports videos for every digital destination. Their cutting-edge AI and Machine Learning technologies analyse live sports broadcasts from over 250 leagues and broadcast partners, including iconic names like the NBA, NHL, ESPN, FIBA and Bundesliga, to create personalized, short-form videos in real-time. The solution empowers media rights owners to unlock new revenue streams and deliver a tailored fan experience across every digital platform. Join the high-profile Engineering team and discover the forefront of sports content innovation.