Senior ML Engineer
About the role
The Senior Machine Learning Engineer will be responsible for designing, building, and scaling LLM-powered and agentic AI systems from prototype to production. The role includes developing retrieval-augmented generation (RAG) pipelines, tool-using agents, evaluation frameworks, and robust serving pipelines to deliver reliable and secure AI solutions. The engineer will collaborate with various teams to create intelligent assistants, workflow automation agents, document understanding pipelines, and decision-support platforms. Key responsibilities include system design, deployment, evaluation, safety enhancements, and mentoring team members. The ideal candidate will have at least 2 years of experience deploying ML systems, strong proficiency in Python, and experience with vector databases and distributed systems.
Must have
- Minimum 2 years of experience in software or machine learning engineering with a proven record of deploying ML systems to production.
- Engineering degree in Computer Science, Electronics, Machine Learning, or a related field.
- Strong hands-on experience building LLM-based products including agents, RAG systems, tool calling, and structured generation.
- Proficiency in Python and solid software engineering practices including clean architecture, testing, and CI/CD.
- Experience with vector databases and search technologies such as pgvector, Pinecone, Weaviate, FAISS, Elasticsearch, or OpenSearch.
- Understanding of distributed systems and production constraints including latency, cost, reliability, and security.
- Practical knowledge of LLM evaluation methodologies and regression prevention.
- Strong communication skills with the ability to lead workstreams end-to-end.
Nice to have
- Experience with model fine-tuning or adaptation (SFT, LoRA/QLoRA) and inference frameworks such as vLLM, TGI, or Triton.
- Familiarity with knowledge graphs and structured knowledge systems (RDF/OWL, Neo4j, entity linking).
- Experience in document AI and multimodal extraction including OCR, layout understanding, and form/table processing.
- Exposure to compliance-heavy domains such as healthcare or finance with privacy and security considerations.
- Experience with agent frameworks such as LangGraph, Autogen, Semantic Kernel, and orchestration patterns.
Technologies
- Agentic AI
- AutoGen
- CI/CD
- Elasticsearch
- Embeddings
- FAISS
- Fine-Tuning
- LangGraph
- LLM
- Machine Learning
- Model Deployment
- Neo4j
- Observability
- OpenSearch
- pgvector
- Pinecone
- Prompt Engineering
- Python
- RAG
- Semantic Kernel
- Triton
- Vector Databases
- vLLM
- Weaviate
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