MLOps / LLMOps Engineer
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* 5+ years of experience in MLOps/LLMOps, building and maintaining production-grade ML systems with a strong focus on Large Language Models (LLMs)
* Excellent knowledge of managing on-premises and/or cloud LLM/MLOps infrastructure in secure hybrid environments
* Excellent knowledge of containerisation and orchestration platforms (e.g. Kubernetes, Docker, Podman, EKS, PKS)
* Good knowledge of MLflow, TensorFlow (TFX) or equivalent ML lifecycle management tools
* Good knowledge of Apache Airflow for workflow orchestration
* Good knowledge of AWS or EU cloud providers
* Good knowledge of Python programming
* Good knowledge of Unix and Bash scripting
* Good knowledge of Agile software development methodologies
* Good knowledge of infrastructure as code tools (Terraform, CloudFormation)
* Good knowledge of messaging services and platforms (e.g. Kafka, Redis, RabbitMQ)
* Experience with CI/CD pipeline design and deployment
* Experience with monitoring and logging tools (e.g. ELK stack, Prometheus, Grafana, OpenTelemetry, CloudWatch)
* Experience with model testing, validation, and deployment in production environments
* Experience with data analytics over big datasets, non-structured databases, and data lakes
* Knowledge of data security measures, encryption mechanisms, and ML security
* Knowledge of NoSQL databases (e.g. OpenSearch, MongoDB)
* Knowledge of query languages (e.g. SQL, Hive, Pig) and information extraction
* Ability to write clear and structured technical documentation
* Bachelor's Degree in Computer Science, Information Technology, Systems Engineering, or a relevant field
* (Desirable) Knowledge of European Commission grant management ecosystem (eGrants)
* Language(s): English (fluent)##
Job Description
* Design, build, and maintain production-grade ML systems with a strong focus on Large Language Models (LLMs) in a secure hybrid on-premises/cloud infrastructure
* Deploy, test, manage, and monitor ML models in different containerised environments
* Work closely with data scientists and back-end developers to build, test, integrate, and deploy ML models
* Evolve the LLMOps lifecycle, including data preparation, fine-tuning, evaluation, deployment, monitoring, and iteration
* Develop evaluation frameworks for LLM quality, robustness, and regression tracking
* Analyse performance metrics and troubleshoot issues to ensure high availability and reliability
* Design and implement CI/CD pipelines using orchestration solutions and data versioning tools
* Create automated anomaly detection systems with constant performance tracking
* Optimise ML pipelines for scalability, efficiency, and cost-effectiveness
* Conduct security studies, security assessments, and address security matters associated with information system projects
* Provide support and guidance to team members on LLMOps/MLOps best practices
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