Description

Key Skills: Prompt Engineering, Generative AI, Azure AI, Microsoft Power Platform, Python, SQL, Java, RAG Pipelines, LangChain, Azure OpenAI, Machine Learning

Good to Have Skills: Microsoft Power Automate, Power Apps, Copilot Studio, Azure AI Foundry, Azure Content Understanding, Azure App Service, GitHub Copilot, SODA Framework, MLflow, Azure Databricks, Semantic Kernel, LlamaIndex, vector search, embeddings, content moderation, A/B testing, data privacy, GDPR, CCPA, responsible AI principles, model risk management, agile delivery methodologies, stakeholder communication, technical documentation.

Roles & Responsibilities:

• Translate business needs into clear functional requirements and prompt design documentation, acceptance criteria, and test cases.
• Interact with business stakeholders to understand automation use cases and triage based on fit for purpose AI implementations.
• Write, version, and optimize prompts to achieve required functionality across use cases including system, user, and tool-calls.
• Conduct development in Microsoft Power Platform tools including Power Automate, Power Apps, Copilot Studio, and Azure AI services.
• Design robust prompt patterns and guardrails for reliability, consistency, and compliance including role prompting and constrained generation.
• Diagnose error modes such as hallucination, drift, and formatting issues and implement mitigation strategies and continuous improvement cycles.
• Maintain strong relationships and provide regular status updates while setting expectations on AI capabilities, constraints, and risks.
• Create and maintain clear functional specifications, prompt playbooks, technical documentation, and operational runbooks for solutions.
• Prepare stakeholder-friendly summaries of findings, performance results, risks, and recommendations for business and technical audiences.
• Contribute to standards for prompt governance, versioning, and reuse across the enterprise to ensure consistency and quality.
• Adhere to data privacy, confidentiality and regulatory requirements relevant to insurance and risk management industry standards.
• Apply responsible AI principles including bias awareness, explainability, auditability and participate in model risk assessments.
• Measure and improve quality using metrics such as precision, recall, accuracy, consistency, latency, and cost optimization.
• Develop evaluation plans and golden datasets to test AI functionality and validate outputs against requirements and source documents.
• Demonstrate ability to read and modify Python scripts, debug minor issues, and write clear, maintainable code with version control.

Experience Required: Mid-Level (1 to 3 Years) with experience using Microsoft's AI tech stack or Senior Level (3 to 6 Years) with hands-on experience in AI/ML engineering and at least 2 years building GenAI/LLM solutions in production.

Education: Bachelor's degree required. Preferred degree in Computer Science, Data Science, Information Systems, or equivalent experience. Microsoft Certified: Azure AI Engineer Associate (AI-102) preferred

Key Skills

Education

Any Graduate