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Tier-1 brand, mid-level ML role, popular AI title, metro location increase applicant density.
Specialized ML/AI and MLOps expertise required, reducing cross-industry transferability.
Explicit 5+ years requirement and many mandatory LLM/MLOps technical skills.
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Develop and deploy Retrieval-Augmented Generation (RAG) AI pipelines using LangChain, LlamaIndex, and vector databases to enable enterprise AI applications.
Design and operationalise Large Language Model (LLM) integrations with Azure OpenAI, Bedrock, and Anthropic APIs, implementing agentic workflows via Model Context Protocol (MCP).
Build MLOps pipelines with MLflow, SageMaker, Azure ML, and transform unstructured R&D data into queryable AI assets while collaborating with domain experts using Scientific ML frameworks.
At least 5 years of professional experience in AI engineering or related fields.
Strong expertise in LLM integration, RAG pipelines (LangChain, LlamaIndex), Model Context Protocol, vector databases (Pinecone, Weaviate, Chroma), and MLOps tools (MLflow, SageMaker, Azure ML, Vertex AI).
Proficient in Python testing for ML pipeline and model validation.
Bachelor’s or Master’s degree in any field.
Technical proficiency focusing on AI/ML engineering with hands-on experience in enterprise-scale LLM integrations and agentic AI systems.
Experience working in a digital engineering or IoT-connected device environment, handling hardware-to-software integration challenges.
Able to collaborate effectively with domain experts applying Scientific ML (PyTorch, JAX) to solve engineering problems.