Agentic AI Engineer

Experience: 8–10 Years

Employment Type: Long term Contract

Location: NY/NJ/

TXJob 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 Lang Graph, Lang Chain, Microsoft Semantic Kernel, Microsoft Agent Framework, Auto Gen, 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, Open Search, 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 Lang Graph, Lang Chain, Semantic Kernel, Microsoft Agent Framework, Auto Gen, 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.