Machine Learning/AI Scientist
acunorNew York, NY
Job Title: Agentic AI Engineer
Experience: 8–10 Years
Employment Type: Long term Contract
Location: NY/NJ/TXJob Summary
We are seeking experiencedAI Engineers / Agentic AI Engineerswith strong hands-on expertise in building enterprise-gradeGenerative AI, Agentic AI, RAG, and LLM-powered applications The ideal candidate will combine strong Python/software engineeringskills 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 fromprototype through production deployment
Key Responsibilities:
- Design, develop, and deploy production-gradeGenerative AI and Agentic AI applications
- BuildAI agents and multi-agent workflowsinvolving reasoning, planning, tool usage, function calling, memory, and workflow automation.
- Develop and optimizeRAG (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 integrateMCP servers, clients, tools, and resourcesfor Agentic AI applications.
- Build AI orchestration workflows using frameworks such asLangGraph, LangChain, Microsoft Semantic Kernel, Microsoft Agent Framework, AutoGen, CrewAI, or equivalent.
- Integrate applications with LLMs such asOpenAI/Azure OpenAI, Anthropic Claude, Google Gemini, Llama, and other commercial or open-source models.
- Implementprompt 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 asAzure AI Search, Pinecone, Weaviate, FAISS, Chroma, OpenSearch, pgvector, or equivalent.
- Design and deploy AI solutions onAzure, AWS, or Google Cloud Platform (GCP) .ImplementLLM 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 yearsof 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 implementingRAG 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 asLangGraph, 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 ofprompt 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 designingMCP servers and clientsand exposing enterprise APIs, tools, or data through MCP.Experience developingmulti-agent architectures and agent orchestration workflows
- Knowledge ofLLM evaluation, tracing, observability, and guardrails
- Experience with AI security and responsible AI practices.
- Experience with
- Docker and Kubernetes
- Familiarity withMLOps / LLMOpspractices.
- Experience with fine-tuning, model customization, or open-source LLMs.
- Experience deploying enterprise-scale AI solutions into production.
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