Job Description
Job Title: Agentic AI Engineer
Experience: 8–10 Years
Employment Type: Long term Contract
Location: NY/NJ/TX
Job Summary
We are seeking experienced AI Engineers / Agentic AI Engineers with strong hands-on expertise in building enterprise-grade Generative AI, Agentic AI, RAG, and LLM-powered applications .
The ideal candidate will combine strong Python/software engineering skills with practical experience developing AI agents, multi-agent workflows, retrieval systems, Model Context Protocol (MCP) integrations, APIs, and cloud-based AI solutions.
This is a hands-on engineering role focused on taking AI solutions from prototype through production deployment .
Key Responsibilities
- Design, develop, and deploy production-grade Generative AI and Agentic AI applications .
- Build AI agents and multi-agent workflows involving reasoning, planning, tool usage, function calling, memory, and workflow automation.
- Develop and optimize RAG (Retrieval-Augmented Generation) solutions using enterprise structured and unstructured data.
- Design and implement integrations using Model Context Protocol (MCP) to enable AI agents to securely interact with enterprise tools, APIs, applications, databases, and data sources.
- Develop and integrate MCP servers, clients, tools, and resources for Agentic AI applications.
- Build AI orchestration workflows using frameworks such as LangGraph, LangChain, Microsoft Semantic Kernel, Microsoft Agent Framework, AutoGen, CrewAI , or equivalent.
- Integrate applications with LLMs such as OpenAI/Azure OpenAI, Anthropic Claude, Google Gemini, Llama , and other commercial or open-source models.
- Implement prompt engineering, embeddings, vector search, retrieval, reranking, context management, structured outputs, and tool/function calling .
- Build backend AI services and REST APIs using Python, FastAPI, Flask, Django , or similar technologies.
- Integrate AI solutions with enterprise applications and external services through APIs, databases, messaging systems, and MCP.
- Work with vector databases and search technologies such as Azure AI Search, Pinecone, Weaviate, FAISS, Chroma, OpenSearch, pgvector , or equivalent.
- Design and deploy AI solutions on Azure, AWS, or Google Cloud Platform (GCP) .
- Implement LLM evaluation, observability, monitoring, guardrails, security, and responsible AI practices .
- Optimize AI applications for performance, scalability, reliability, latency, and cost.
- Collaborate with product, engineering, architecture, data, and business teams to translate business use cases into scalable AI solutions.
Required Qualifications
- 8–10 years of overall experience in Software Engineering, Data Engineering, Machine Learning, or AI Engineering.
- Strong hands-on programming experience with Python .
- Strong recent experience developing Generative AI / LLM-powered applications .
- Hands-on experience building Agentic AI solutions or AI agents .
- Strong experience implementing RAG architectures and retrieval pipelines .
- Practical experience with Model Context Protocol (MCP) and agent-to-tool/application integrations.
- Experience with at least one AI orchestration/agent framework such as LangGraph, LangChain, Semantic Kernel, Microsoft Agent Framework, AutoGen, CrewAI , or equivalent.
- Experience working with OpenAI/Azure OpenAI, Anthropic Claude, Gemini, Llama , or similar LLMs.
- Strong understanding of prompt engineering, embeddings, vector search, tool/function calling, structured outputs, retrieval, reranking, and context management .
- Experience developing APIs and integrating AI solutions with enterprise applications and data sources.
- Hands-on experience with at least one major cloud platform: Azure, AWS, or GCP .
- Strong understanding of software engineering practices including Git, CI/CD, testing, debugging, and production deployment .
Preferred Qualifications
- Experience designing MCP servers and clients and exposing enterprise APIs, tools, or data through MCP.
- Experience developing multi-agent architectures and agent orchestration workflows .
- Knowledge of LLM evaluation, tracing, observability, and guardrails .
- Experience with AI security and responsible AI practices.
- Experience with Docker and Kubernetes .
- Familiarity with MLOps / LLMOps practices.
- Experience with fine-tuning, model customization, or open-source LLMs.
- Experience deploying enterprise-scale AI solutions into production.
Job Tags
Long term contract