AI Engineer

Job Description

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

Are you interested in this position?
Apply by clicking on the “Apply Now” button below!
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