AI Engineer

Roles and 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.Design, develop, test, deploy and maintain large language models using Python and various AI frameworks.
• Collaborate with cross-functional teams to integrate machine learning algorithms into existing systems on AWS Cloud or Azure Cloud platforms.
• Develop automated testing scripts using CI/CD pipelines to ensure high-quality software releases.
• Troubleshoot issues related to model performance, data quality, and system reliability.

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..