AI Engineer

Role & 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.

Skills (Required + Good to have)

• 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.

• Cloud: AWS (S3, IAM, CloudWatch) with SageMaker for training/inference and

deployment patterns.

• Good to have: Azure ecosystem familiarity (data/AI services) and hybrid cloud

architectures.

• Good to have: ETL concepts and tools; familiarity with AWS Glue and data pipeline

patterns.