AI Solution Engineer - No Moonlighting candidates.

Full Time Position with MNC

Role:

- Own AI requirements development and technical solutioning for AI RFPs/proposals and internal AI initiatives.
- Build agent-driven workflows (Copilot Studio) and deploy AI/GenAI components on cloud platforms (Azure mandatory & AWS positive to have) to production standards.

Responsibilities:

- Conduct stakeholder discovery and translate business goals into AI use cases, requirements, and acceptance criteria.
- Define data requirements: sources, access, quality, governance and retention.
- Design GenAI/ML approaches (RAG, fine-tuning, agent workflows) with clear assumptions and tradeoffs.
- Create evaluation criteria and validation plans (offline tests, human review, regression).
- Break down AI RFP/RFI requirements into scope, risks, dependencies, and level-of-effort estimates.
- Write proposal-ready technical narratives: architecture, methodology, implementation plan, and MLOps/LLMOps.
- Build rapid demos/POCs to validate feasibility (retrieval, tool/function calling,



integrations).
- Develop and orchestrate agents in Microsoft Copilot Studio (connectors, actions, governance).
- Implement and deploy solutions on AWS/Azure; leverage SageMaker and cloud-native services for scalable inference.
- Collaborate with SMEs and delivery teams to create reusable assets (templates, prompts/modules) and smooth handoffs.

Tech Stack & Skills:

- Strong Python development;

experience building APIs/services (e.g., FastAPI/Flask) and integrating enterprise systems.

- GenAI systems: RAG pipelines, prompt/tool routing, grounding/guardrails, and evaluation frameworks.
- Mandatory: Cloud Azure ecosystem familiarity (data/AI services) and hybrid cloud architectures.
- Good to have: Cloud AWS (S3, IAM, CloudWatch) with SageMaker for training/inference and deployment patterns.
- Good to have: ETL concepts and tools; familiarity with AWS Glue and data pipeline patterns.

📌 AI Solution Engineer - GenAI/Cloud (India)
🏢 Rnrcertification
📍 India