Design and optimize prompts, agent architectures, MCP tools, RAG workflows, and context-management strategies for reliable AI applications. Develop and integrate LLM-powered solutions with enterprise systems, APIs, healthcare data, and backend services. Establish evaluation and testing frameworks, improve response quality, and support deployment, monitoring, troubleshooting, security, compliance, governance, and responsible AI practices.
Role Summary:
AI Engineer with strong hands-on experience in prompt engineering, LLM response optimization, and AI application development. The ideal candidate can design prompts, improve model output quality, help LLMs understand user input, and build reliable, context-aware AI solutions.
Key Responsibilities
- Design, develop, and optimize prompts for Large Language Models (LLMs) to improve accuracy, reliability, and business outcomes.
- Build and configure Agentic AI solutions that leverage planning, reasoning, memory, and multi-step task execution capabilities.
- Develop and integrate MCP (Model Context Protocol) tools, enabling AIagents to securely discover and interact with enterprise systems, APIs, and data sources.
- Design agent architectures, tool-calling frameworks, retrieval mechanisms, and context management strategies.
- Collaborate with product managers, architects, and engineering teams to translate business requirements into AI-driven solutions.
- Implement Retrieval-Augmented Generation (RAG), knowledge grounding, and context orchestration patterns.
- Define evaluation frameworks and prompt testing methodologies to measure agent performance, quality, and reliability.
- Ensure AI solutions adhere to security, compliance, governance, and responsible AI standards.
- Support deployment, monitoring, troubleshooting, and continuous improvement of AI agents and MCP-enabled workflows.
- Contribute to architecture reviews, technical design documentation, and engineering best practices for AI platforms.
Required Skill
- Hands-on experience with LLMs such as GPT, Claude, Gemini, Llama, or similar models.
- Strong prompt engineering skills, including prompt templates, system prompts, few-shot prompting, and response tuning.
- Develop AI-powered applications using LLM APIs, RAG workflows, enterprise data sources, and backend services.
- Experience with LLM APIs and application integration using Python, Java, JavaScript, or similar programming languages.
- Knowledge of RAG, embeddings, vector databases, semantic search, and knowledge retrieval workflows.
- Ability to evaluate and improve AI responses for accuracy, relevance, completeness, tone, and instruction following.
- Understanding of NLP concepts, hallucination mitigation, prompt injection risks, guardrails, and responsible AI practices.
- Knowledge of healthcare data privacy and secure sensitive-data handling, including HIPAA-aligned PHI/PII practices, data minimization, de-identification, masking, access controls, auditability, and responsible use of healthcare data in AI workflows.
- Experience with MCP servers, cloud platforms, APIs, backend services, and production deployment is preferred.
- Experience integrating AI agents with healthcare, health records, or regulated industry systems.
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