Job Description
We are seeking an experienced AI Engineer with strong hands-on experience in Prompt Engineering, LLM application development, Agentic AI, RAG, and AI response optimization .
The ideal candidate will have experience designing reliable, context-aware AI solutions, integrating LLMs with enterprise systems, developing AI agents and tool-calling workflows, and improving model response quality, accuracy, and reliability.
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 supporting planning, reasoning, memory, and multi-step task execution.
- Develop and integrate MCP (Model Context Protocol) tools that enable AI agents to securely interact with enterprise systems, APIs, and data sources.
- Design agent architectures, tool-calling frameworks, retrieval mechanisms, and context-management strategies.
- Develop AI-powered applications using LLM APIs, RAG workflows, enterprise data sources, and backend services.
- Implement Retrieval-Augmented Generation (RAG), knowledge grounding, embeddings, vector databases, and semantic search .
- Evaluate and improve LLM responses for accuracy, relevance, completeness, tone, and instruction following.
- Develop evaluation frameworks and prompt-testing methodologies to measure AI agent performance, quality, and reliability.
- Apply NLP concepts, hallucination mitigation techniques, prompt-injection protection, guardrails, and responsible AI practices.
- Collaborate with product managers, architects, and engineering teams to translate business requirements into AI-driven solutions.
- Ensure AI solutions follow 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 AI engineering best practices.
Required Skills
- 9+ years of professional IT/software engineering experience.
- Strong hands-on Prompt Engineering experience, including prompt templates, system prompts, few-shot prompting, and response tuning.
- Hands-on experience with LLMs such as GPT, Claude, Gemini, Llama , or similar models.
- Experience developing AI applications using LLM APIs, RAG workflows, enterprise data sources, and backend services .
- Strong programming experience with Python, Java, JavaScript, or similar languages .
- Knowledge of RAG, embeddings, vector databases, semantic search, and knowledge retrieval workflows .
- Experience evaluating and optimizing LLM responses.
- Understanding of NLP, hallucination mitigation, prompt injection risks, guardrails, and responsible AI .
- Experience integrating AI solutions with APIs, backend services, and enterprise systems.
- Knowledge of healthcare data privacy and secure sensitive-data handling, including:
- HIPAA-aligned practices
- PHI/PII protection
- Data minimization
- De-identification
- Data masking
- Access controls
- Auditability
- Responsible use of healthcare data in AI workflows
Preferred Skills
- Experience with MCP servers / Model Context Protocol .
- Experience building and deploying Agentic AI solutions in production.
- Experience with cloud platforms and production AI deployments.
- Experience integrating AI agents with healthcare systems, electronic health records, or other regulated-industry systems.
- Experience with healthcare data and regulated enterprise environments.
- Experience with AI evaluation frameworks and automated prompt testing.
- Experience with AI architecture, technical design, and production monitoring.
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