EMBEDDED AI ENGINEERING POD

AI Implementation Engineer Role

Level: AI Implementation Engineer Senior / Advanced - 6+ years

Practice: Wissen GenAI

Locations: Mumbai / Bengaluru / New York - hybrid, embedded with delivery teams

Reports to: EMBEDDED AI PRACTICE Senior AI Engineering Specialist (Architect); Wissen GenAI Program Lead

Embedded inside enterprise delivery teams, you work closely with global, cross-regional teams to turn prioritized GenAI use cases into production software - building, integrating, and hardening Azure-based AI solutions and accelerating adoption within the teams you join.

You deliver production software and help the teams you join work faster.

As an embedded AI Implementation Engineer, you help convert prioritized use cases into shipped, governed, measurable software.

Key responsibilities

1. Build and ship.

Implement GenAI features end to end on Azure - RAG pipelines, agents, APIs, and UI integrations - against enterprise systems and data.

2. Embed and enable.

Work inside the delivery pods: pair with their engineers, remove blockers, and transfer GenAI skills so adoption sticks after you move on.

3. Productionize.

Add evaluation, observability, guardrails, caching, and CI/CD so prototypes become reliable, cost-productive services.

4. Integrate securely.

Connect to enterprise data with correct access control, secrets management, and compliance with enterprise security standards and handling of sensitive data.

5. Iterate on quality.

Use evaluation results and user feedback to improve grounding, accuracy, latency, and cost.

6. Measure.

Track delivery and quality metrics that roll up to the program's targets.

Must-have qualifications





- 6+ years in software engineering, with 2+ years building GenAI/LLM applications in production.
- Strong Python (incl. async) and Java (the primary enterprise application stack; Spring a plus); solid API and systems design.
- Azure GenAI hands-on: Azure OpenAI, Azure AI Foundry, Azure AI Search for RAG, Azure AI Document Intelligence (IDP), and Prompt Flow.
- Agent frameworks: Microsoft Agent Framework / Semantic Kernel / AutoGen (or LangChain / LangGraph) and tool / function calling.

Preferred

- RAG fundamentals: embeddings, chunking, vector search, reranking, and grounding.
- Data platforms: Snowflake including Cortex AI (Cortex Search, LLM functions) and SQL, for accessing and grounding on enterprise data.
- Prompt engineering as versioned code; building and running evaluations.
- DevOps: Azure DevOps / GitHub Actions, Docker, AKS / Azure Functions, and observability.
- Financial services or other regulated environments.
- Front-end (React) for AI-assisted UX; streaming and token level operations.
- Azure AI Content Safety and responsible-AI practices.
- Certification: Azure AI Engineer Associate.

What success looks like - first 6 to 12 months

- Multiple GenAI features shipped to production within the embedded delivery pods.
- Measurable adoption and productivity uplift in the teams you support.
- Reusable components adopted from the architects' reference framework.
- Explicit contribution to faster time-to-market and lower defect rates.

Skills:- Artificial Intelligence (AI), Python, Generative AI, Agentic AI, Large Language Models (LLM), Retrieval Augmented Generation (RAG), Snow flake schema, Azure OpenAI, LangGraph and LangChain

📌 AI Implementation Engineer (Mumbai)
🏢 wissen technology
📍 Mumbai