AI Engineer

Job description

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.