AI Engineer | Onsite

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.