AI Prompt Engineer

Job Description
Must Have Technical/Functional Skills

Objective of Role: We are seeking a highly skilled Prompt Engineering & AI Solutions Specialist to design, develop, and optimize enterprise-grade AI solutions using frontier large language models (LLMs). The ideal candidate will have hands-on experience with Microsoft Copilot Studio, Azure OpenAI, and advanced prompt engineering techniques to build secure, scalable, and compliant AI-powered assistants and workflows for banking and financial services use cases.

This role will work closely with product owners, architects, risk/compliance teams, and business stakeholders to operationalize Generative AI across infrastructure, operations, and business functions.

Primary Skill

• Strong hands-on experience with Microsoft Copilot Studio.
• Proven experience using Azure OpenAI / OpenAI APIs.
• Practical experience with frontier LLMs (GPT 4 class or equivalent).
• Solid understanding of prompt engineering frameworks and design patterns.
• Hands-on experience with RAG architectures and vector search technologies.
• Strong knowledge of tokenization, context windows, and inference latency tradeoffs.
• Proficiency in Python for AI integration and automation.
• Experience working with REST APIs, JSON, and YAML.
• Experience with cloud platforms, preferably Microsoft Azure.
• Familiarity with AI governance, Responsible AI, and model risk management.
• Prior experience delivering enterprise copilots or chatbots at scale.

Secondary / Nice to Have Skills

• Experience with Power Platform (Power Apps, Power Automate).
• Exposure to model evaluation, monitoring, and AI observability tools.
• Knowledge of AI ethics and regulatory expectations in BFSI environments.

Experience:

Minimum 6+ years

Roles & Responsibilities

• Design, test, and refine advanced system, user, and tool prompts for frontier LLMs (GPT 4/4.1 class).
• Apply prompt engineering patterns including few shot, zero shot, ReAct, and controlled chain of thought.
• Implement Retrieval Augmented Generation (RAG) using vector search and enterprise knowledge sources.
• Continuously optimize prompts for accuracy, consistency, latency, and cost efficiency.
• Build and deploy AI assistants using Microsoft Copilot Studio.
• Integrate copilots with enterprise APIs, workflows, and data sources.
• Implement responsible AI controls including content filtering, grounding, and hallucination reduction.
• Enforce data privacy, PII masking, and access control in AI workflows.
• Collaborate with Risk, Legal, and Compliance teams to meet regulatory and audit requirements.
• Support model governance, SDLC compliance, and production readiness reviews.
• Document prompt frameworks, AI workflows, and operational standards.