MLOPS Engineer
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Key Responsibilities
• Design, build, and maintain production‑grade ML systems centered on LLMs across hybrid on‑prem/cloud infrastructure.
• Deploy, test, monitor, and manage ML models in containerised environments.
• Develop and evolve the LLMOps lifecycle, including data preparation, fine‑tuning, evaluation, deployment, and monitoring.
• Create CI/CD pipelines, orchestration solutions, and data versioning tools for ML workflows.
• Implement automated anomaly detection, performance tracking, and cost‑optimisation for ML pipelines. Required Qualifications
• Strong expertise in managing on‑premise and/or cloud LLM/MLOps infrastructure.
• Proficiency with containerisation and orchestration platforms such as Kubernetes, Docker, Podman, EKS, PKS.
• Hands‑on experience with MLflow, TensorFlow (TFX), Airflow, and major cloud providers (AWS or EU cloud).
• Solid programming skills in Python, Unix/Bash, and infrastructure‑as‑code tools like Terraform or CloudFormation.
• Familiarity with messaging systems (Kafka, Redis, RabbitMQ) and NoSQL databases (OpenSearch, MongoDB). Nice to have
• Knowledge of encryption mechanisms and ML security best practices.
• Experience with the European Commission grant management ecosystem (eGrants).
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