MLOps Engineer

MLOps Engineer to product ionize and scale ML and GenAI systems, with a focus on LLM deployment, orchestration, and reliability in production environments.

Deploy, manage, and scale ML/DL models in production

Build and operate Kubernetes-based infrastructure for ML workloads

Handle model packaging, serialization, and versioning

Design scalable inference systems (batch and real-time)

Deploy and optimize local LLMs (latency, throughput, cost)

Build and manage agentic systems with tool integration

Design and manage LLM memory (short-term, long-term, vector stores)

Integrate and manage API gateways for model access, routing, and rate limiting

Monitor performance, drift, and system reliability

Hands-on experience with ML/DL models and serialization

Proven experience in model deployment, scaling, and monitoring

Experience with local LLM deployment and optimization

Solid understanding of LLM memory patterns (context windows, retrieval, persistence)

Experience with API gateways, load balancing, and service routing

Familiarity with GenAI workflows (RAG, orchestration frameworks)

Experience building agentic / multi-step LLM systems

Proficiency in Python and modern ML/infra tooling