MLOps Engineer

Bachelor’s degree or higher in Computer Science, Software Engineering, or a related field.

3+ years of experience in machine learning and Python programming

Experience working with ML models in production environments.

Experience with LLMs, RAG pipelines, or generative AI systems.

Familiarity with observability tools such as Prometheus, Grafana, or OpenTelemetry.

• Build, manage, and scale MLOps workflows and pipelines for model deployment, monitoring, and automation.

• Collaborate with data scientists and ML engineers to productionize ML and LLM models, including retrieval-augmented generation (RAG) or similar architectures, across development, staging, and production environments.

• Design and implement CI/CD pipelines for ML, covering code, data, and model lifecycles, including automated testing, model validation, and secure artifact promotion.

• Monitor model and data health in production, including performance, drift, data quality, latency, and cost.

• Define service-level objectives (SLOs) and alerts using observability tools such as Prometheus, Grafana, and OpenTelemetry.

• Manage and scale ML infrastructure on cloud platforms (AWS, GCP, or Azure).

• Work with containers and orchestration tools including Docker and Kubernetes.

• Optimize Python-based components for performance and scalability.

• Optimize infrastructure to support high-performance model execution and scalable experimentation.

• Define standards, best practices, and reusable components for the end-to-end MLOps lifecycle.

• Create and maintain documentation, including ML workflows, runbooks, and operational playbooks.

• Ensure compliance with data privacy, responsible AI, and governance requirements.

We are looking for a motivated MLOps Engineer with strong Python skills who can build, deploy, and maintain scalable ML/LLM pipelines in cloud environments, with a focus on automation, reliability, and collaboration.