Agentic AI Engineer

Job Title: Agentic AI Engineer Experience: 8–10 Years Employment Type: Long term Contract Location: NY/NJ/TX

Please make an application promptly if you are a good match for this role due to high levels of interest.

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. xsgimln Experience deploying enterprise-scale AI solutions into production.