Senior ML Engineer

Keywords

LLMOps, Large Language Models, Model Serving, MLOps, Agentic Systems, RAG, Evaluation, Monitoring, Production ML, Docker, Kubernetes, Python

Responsibilities and Expectations

Design, build, and maintain production-grade ML systems with a strong focus on Large Language Models (LLMs).

Own and evolve the LLMOps lifecycle: data preparation, fine-tuning, evaluation, deployment, monitoring, and iteration.

Develop evaluation frameworks for LLM quality, robustness, and regression tracking.

Collaborate closely with researchers, product engineers, mathematicians, and infrastructure teams to translate research prototypes into reliable production systems.

Contribute to architectural decisions around agentic systems, RAG pipelines, and hybrid ML + symbolic components.

Experience 3+ years of experience in Machine Learning or Applied AI roles.

Hands-on experience deploying ML models to production environments.

Practical experience with LLMs (open-source or proprietary) in real-world applications.

Experience operating ML systems under production constraints (latency, cost, observability, reliability).

Experience working in fast-moving startup or R&D-driven environments is a strong plus.

Technical skills

Strong Python proficiency; experience with ML frameworks.

Solid understanding of modern LLM stacks: fine-tuning, inference optimization, RAG, prompt/agent orchestration.

Experience with MLOps / LLMOps tooling: experiment tracking, evaluation pipelines, monitoring, CI/CD for models.

Familiarity with containerization and deployment (Docker, Kubernetes or equivalents).

Experience with cloud or on-prem GPU environments.

Understanding of distributed systems concepts is a plus.