Senior AI Engineer - GenAI Solutions

Responsibilities:
Design and build production GenAI applications, LLM copilots, RAG pipelines, agentic workflows, and AI-native UX on our 2026 stack.
Engineer retrieval end-to-end: chunking, embeddings, vector stores, retrieval, re-ranking, and hybrid search, and debug retrieval quality when it matters.
Build agents and multi-step workflows with LangGraph and LangChain; integrate MCP servers and agent SDKs for tool access.
Augment enterprise platforms Clarity PPM, Medallia, ServiceNow, Salesforce with predictive insight, intelligent automation, and conversational UX.
Deploy to AWS Bedrock, Azure AI Foundry, or GCP Vertex AI with solid CI/CD, observability, and guardrails.
Build evaluation harnesses, wire up LLM observability, and bake in responsible-AI basics: PII handling, guardrails, and audit trails.
Consult and Deliver:
Run client discovery workshops and translate business problems into scoped, ROI-backed AI use cases.
Demo working software to technical and business stakeholders, and defend your design decisions and tradeoffs.
Tie every engagement to a measurable KPI: revenue lift, cost-to-serve, cycle time, adoption, or churn.
Requirements:
4-7 years software engineering: You've shipped and maintained real production systems, not just prototypes.
Python mastery: Primary language for our AI services. Clean, testable, production Python (FastAPI or similar).
Production GenAI experience: You've shipped at least one real LLM-powered feature to users: prompt design, structured outputs, tool/function calling, streaming.
RAG engineering: Chunking, embeddings, vector DBs (Pinecone, Weaviate, pgvector, or Qdrant), retrieval, re-ranking, hybrid search. You can diagnose bad retrieval.
Agentic systems: Built agents or workflows with LangGraph, LangChain, or LlamaIndex. You understand state, tools, and routing and what MCP and agent SDKs are for.
Full-stack capability: Comfortable building AI-native UX with TypeScript / React / Next.js enough to ship a working product, not just an API.
Cloud AI deployment Hands-on with at least one of AWS Bedrock, Azure AI Foundry, or GCP Vertex AI. Docker, CI/CD, and APIs shipped to production.
Evaluation and trust: You build eval harnesses, use LLM observability (LangSmith, Langfuse, or Arize), and handle PII and guardrails responsibly.
Client-facing communication- You can run a workshop, scope a use case, demo to stakeholders, and articulate ROI in plain business language. Fluent written and spoken English.

Experience
4-7 yrs