Data Engineer

We are looking for a MLOps / AIOps / LLMOps / AgentOps Engineer to join a multidisciplinary Data & AI team. The main mission of this role is to design operate and continuously evolve our AIOps platform ensuring that our AI products run in a reliable scalable and costefficient way.

This position is strongly focused on platform infrastructure automation observability and operations rather than on building ML models or AI products themselves.

You will work with modern cloud technologies (mainly AWS with some Azure exposure) and collaborate closely with Data Scientists Data Engineers and Product teams to bring AI solutions into production and keep them running smoothly.

We are open to candidates with strong expertise in at least one core area (e.g. cloud DevOps platform engineering or ML operations) and solid foundational knowledge in the others with motivation to grow across the full AI operations stack.

Key Responsibilities

• Design maintain and evolve the AIOps platform supporting:
  • Traditional machine learning models in production
  • LLMbased solutions such as RAG pipelines and AI Agents
  • Speech Analytics use cases (ASR conversation analysis NLP)
• Build and operate ML and LLM pipelines with a strong focus on:
  • Reliability automation and observability
  • Model and LLM quality performance and drift monitoring
  • Cloud cost control and optimization
• Implement LLMOps / AgentOps practices including:
  • LLM evaluation and observability
  • Prompt management traceability and specialized logging
  • Agent integration orchestration and lifecycle management
• Ensure continuous operation of AI products including:
  • Alerts dashboards SLOs / SLIs
  • Scalability strategies and basic autoremediation mechanisms
• Manage deployments in cloud environments (AWS / Azure) and container platforms (Docker / Kubernetes)
• Collaborate closely with Data Scientists and Data Engineers to productionize robust scalable AI solutions
• Contribute to internal standards automation and best practices across the AI and data ecosystem

Required Skills (Must Have)

• Handson experience in MLOps AIOps or operating ML systems in production
• Solid understanding of LLMOps and AgentOps concepts (RAGs agents evaluation monitoring)
• Experience working with AWS and/or Azure in production environments
• Practical knowledge of containers and Kubernetes (Docker basic Helm usage etc.)
• Experience with CI/CD pipelines (GitHub Actions GitLab CI Azure DevOps Jenkins or similar)
• Familiarity with observability and monitoring concepts (CloudWatch OpenTelemetry Prometheus etc.)
• Experience managing infrastructure as code (Terraform Bicep CDK or similar)
• Python experience and familiarity with the ML ecosystem (e.g. scikitlearn PyTorch) even if not a Data Scientist
• Good understanding of the ML / LLM lifecycle from development to production and monitoring
• Fluent English to work in an international environment

Nice to Have (Not Required but Valuable)

• Experience with ML/AI platforms such as SageMaker Azure ML MLflow Kubeflow
• Exposure to Speech Analytics technologies (ASR diarization conversational NLP)
• Experience with cloud cost optimization / FinOps especially for AI workloads
• Experience building or operating AI agents copilots or conversational systems
• Familiarity with LLM frameworks (LangChain LlamaIndex Semantic Kernel etc.)
• Experience with workflow and orchestration tools (Airflow Argo Step Functions Durable Functions)

Professional Skills & Mindset

• Strong focus on reliability automation and scalability
• Ability to collaborate effectively in multidisciplinary teams
• Clear communication and documentationoriented mindset
• Platform mindset: building reusable maintainable and robust solutions
• Proactive analytical and continuousimprovement driven
• Strong sense of ownership and endtoend responsibility
• Motivation to learn and grow across the AI operations stack

Technology Environment

• Cloud: AWS Azure
• Orchestration & Containers: Kubernetes Docker
• CI/CD: GitHub Actions GitLab CI Azure DevOps
• Observability: Prometheus Grafana ELK/EFK OpenTelemetry
• Infrastructure as Code: Terraform Bicep CloudFormation
• AI / ML Tools: MLflow Azure ML SageMaker LangChain LlamaIndex Semantic Kernel
• Primary Language: Python