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Tier‑1 firm, mid‑level AI role in metro with broad applicant interest despite niche LLM skills.
Core ML/LLM skills transfer well, though scientific ML for engineering raises some domain specificity.
Explicit 5+ years plus mandatory LLM, MLOps and vector-database skills makes filters stringent.
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Develop and deploy retrieval-augmented generation (RAG) pipelines leveraging LangChain, LlamaIndex, and vector databases for enterprise AI solutions.
Design and operationalize large language model (LLM) integrations with platforms such as Azure OpenAI, Bedrock, and Anthropic.
Build MLOps pipelines using MLflow, SageMaker, Azure ML, and transform unstructured R&D data into queryable AI assets in collaboration with domain experts.
5+ years of relevant work experience in AI engineering or related fields.
Proven expertise with LLM integration and fine-tuning, RAG pipelines (LangChain, LlamaIndex), and vector databases (Pinecone, Weaviate, Chroma).
Experience with MLOps tools including MLflow, SageMaker, Azure ML, and Python testing for ML pipelines.
Educational qualification: Bachelor’s or Master’s degree (field not specified).
Experienced in building agentic AI workflows using Model Context Protocol (MCP) for enterprise-scale applications.
Proficient in advanced AI/ML technologies including Scientific ML frameworks like PyTorch, JAX, and graph neural networks (preferred).
Capable of transforming complex unstructured data into operational AI systems within a collaborative domain expert environment.