MLOps / LLMOps Engineer (AI Platform Engineer)
⚲ Warszawa
160 - 180 PLN/h netto (B2B)
Wymagania
- Docker
- Kubernetes
- Azure
- Python
- Bash
Opis stanowiska
📍 Location: Warsaw, Poland (hybrid – minimum 1 day per week onsite, from city center office)
💰 Salary: up to 180 PLN/h
📅 Start: 1st of July
🔧 About the role
We are looking for an experienced MLOps / LLMOps Engineer to join a large-scale AI initiative in the insurance sector. The role focuses on building and operating a production-grade AI platform, including MLOps/LLMOps infrastructure, automation of ML workflows, and ensuring compliance with regulatory requirements (including AI Act).
You will work at the intersection of Data Science, DevOps, and IT Operations, enabling reliable deployment and monitoring of ML and GenAI models in a hybrid cloud environment.
🧠 Key responsibilities
• Design and build scalable AI/MLOps/LLMOps platform in Azure environment
• Develop and maintain infrastructure for training and serving ML/GenAI models (Azure ML, Azure AI Foundry, AKS, Kubernetes)
• Build and automate CI/CD/CT pipelines for ML workflows
• Implement model versioning, testing, and deployment automation
• Manage Docker-based deployments and Kubernetes orchestration (hybrid cloud + on-prem integration)
• Implement monitoring, logging, alerting, and drift detection for AI models
• Ensure governance, auditability, lineage tracking, and AI Act compliance
• Optimize performance, scalability, and cost of AI infrastructure
• Ensure high availability and stability of production AI services
• Collaborate with Data Science and Infrastructure teams in Agile environment
🧰 Requirements
• 3+ years of experience in DevOps, MLOps, or Software Engineering with ML in production
• Strong hands-on experience with Docker and Kubernetes (Helm, Ingress, cluster management)
• Strong experience with Microsoft Azure (Azure ML, AKS, Azure Container Registry)
• Experience building CI/CD pipelines (Azure DevOps, GitHub Actions, Jenkins)
• Strong Python and Bash/Shell scripting skills
• Experience with MLflow, Kubeflow or similar MLOps tools
• Experience with Infrastructure as Code (Terraform, Bicep or Ansible)
• Experience working with hybrid cloud / on-prem environments
• Understanding of monitoring and observability in production systems
• Ability to work across Data Science and IT Operations teams
• Services provided from Poland
⭐ Nice to have
• Azure certifications (AZ-400, AI-102)
• Experience with LLMs and RAG architectures
• Knowledge of Prometheus, Grafana, Azure Monitor
• Understanding of hybrid networking (VNet, VPN, Private Endpoints)
• Experience with vector databases and Azure AI Search
• Knowledge of AI governance and compliance (AI Act)
📩 Sounds interesting?
Contact me:
julia.lanckamer@awareson.com
+48 600 530 122
LinkedIn: https://www.linkedin.com/in/julia-lanckamer-47ab1a213/
Awareson is an IT Recruitment Consultancy specializing in delivery of IT Specialists on contract and permanent basis to Customers across Europe.
💰 Salary: up to 180 PLN/h
📅 Start: 1st of July
🔧 About the role
We are looking for an experienced MLOps / LLMOps Engineer to join a large-scale AI initiative in the insurance sector. The role focuses on building and operating a production-grade AI platform, including MLOps/LLMOps infrastructure, automation of ML workflows, and ensuring compliance with regulatory requirements (including AI Act).
You will work at the intersection of Data Science, DevOps, and IT Operations, enabling reliable deployment and monitoring of ML and GenAI models in a hybrid cloud environment.
🧠 Key responsibilities
• Design and build scalable AI/MLOps/LLMOps platform in Azure environment
• Develop and maintain infrastructure for training and serving ML/GenAI models (Azure ML, Azure AI Foundry, AKS, Kubernetes)
• Build and automate CI/CD/CT pipelines for ML workflows
• Implement model versioning, testing, and deployment automation
• Manage Docker-based deployments and Kubernetes orchestration (hybrid cloud + on-prem integration)
• Implement monitoring, logging, alerting, and drift detection for AI models
• Ensure governance, auditability, lineage tracking, and AI Act compliance
• Optimize performance, scalability, and cost of AI infrastructure
• Ensure high availability and stability of production AI services
• Collaborate with Data Science and Infrastructure teams in Agile environment
🧰 Requirements
• 3+ years of experience in DevOps, MLOps, or Software Engineering with ML in production
• Strong hands-on experience with Docker and Kubernetes (Helm, Ingress, cluster management)
• Strong experience with Microsoft Azure (Azure ML, AKS, Azure Container Registry)
• Experience building CI/CD pipelines (Azure DevOps, GitHub Actions, Jenkins)
• Strong Python and Bash/Shell scripting skills
• Experience with MLflow, Kubeflow or similar MLOps tools
• Experience with Infrastructure as Code (Terraform, Bicep or Ansible)
• Experience working with hybrid cloud / on-prem environments
• Understanding of monitoring and observability in production systems
• Ability to work across Data Science and IT Operations teams
• Services provided from Poland
⭐ Nice to have
• Azure certifications (AZ-400, AI-102)
• Experience with LLMs and RAG architectures
• Knowledge of Prometheus, Grafana, Azure Monitor
• Understanding of hybrid networking (VNet, VPN, Private Endpoints)
• Experience with vector databases and Azure AI Search
• Knowledge of AI governance and compliance (AI Act)
📩 Sounds interesting?
Contact me:
julia.lanckamer@awareson.com
+48 600 530 122
LinkedIn: https://www.linkedin.com/in/julia-lanckamer-47ab1a213/
Awareson is an IT Recruitment Consultancy specializing in delivery of IT Specialists on contract and permanent basis to Customers across Europe.
🔍 Dekoder Ogłoszenia
🔴
minimum 1 day per week onsite, from city center office
Praca hybrydowa, ale wymaga regularnej obecności w biurze, co może być uciążliwe dla osób preferujących pracę zdalną.
🟡
production-grade AI platform
Oznacza, że platforma musi być stabilna, skalowalna i bezpieczna, co może wymagać więcej pracy niż w przypadku projektów deweloperskich.
🟡
compliance with regulatory requirements (including AI Act)
Wymaga dogłębnej znajomości przepisów dotyczących AI i ich implementacji, co może być złożone i czasochłonne.
🟡
hybrid cloud environment
Praca z infrastrukturą rozproszoną między chmurą a środowiskiem lokalnym, co może komplikować zarządzanie i rozwiązywanie problemów.
🟡
Agile environment
Praca w metodyce zwinnej, co oznacza częste zmiany priorytetów i potrzebę szybkiej adaptacji.