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 good 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.