Tarrytown
We are seeking an experienced Programmer/Analyst with a strong focus on Generative AI and Agentic AI to design, develop, and deliver enterprise-grade AI solutions across complex business environments. \n The ideal candidate will bring 10+ years of technology and solution delivery experience, with deep hands-on expertise in modern Generative AI architectures, multi-agent systems, Retrieval-Augmented Generation, natural language processing, forecasting, recommendation systems, and enterprise AI integration. \n This individual will work closely with business stakeholders, architects, data scientists, engineers, and cross-functional technology teams to translate business needs into scalable, secure, production-ready AI capabilities. \n The role requires someone who can operate independently, move comfortably between business requirements and technical implementation, and take ownership of AI solutions from use-case definition through architecture, development, integration, testing, and deployment. \n \n Key Responsibilities \n\n • Design and develop enterprise Generative AI and Agentic AI solutions aligned with business and technology requirements.\n, • Architect and implement multi-agent AI systems that coordinate specialized agents, workflows, tools, data sources, and enterprise applications.\n, • Build production-ready Retrieval-Augmented Generation, RAG, solutions, including document ingestion, chunking, embeddings, retrieval strategies, grounding, prompt orchestration, and response generation.\n, • Develop conversational AI and chatbot capabilities using enterprise data and approved knowledge sources.\n, • Design AI solutions supporting use cases such as forecasting, recommendation engines, intelligent search, NLP, summarization, knowledge retrieval, workflow automation, and decision support.\n, • Develop and orchestrate AI workflows using frameworks such as LangChain, LangGraph, CrewAI, and Model Context Protocol, MCP.\n, • Integrate large language models with APIs, enterprise systems, databases, tools, and business workflows.\n, • Design and implement vector search capabilities using vector databases and cloud-native AI services within AWS and/or Microsoft Azure environments.\n, • Evaluate and select appropriate LLMs, embedding models, retrieval techniques, orchestration patterns, and agent architectures based on business requirements.\n, • Translate ambiguous business problems into clearly defined AI use cases, technical requirements, solution architectures, and implementation plans.\n, • Partner with stakeholders to assess use-case feasibility, value, risks, dependencies, and implementation considerations.\n, • Develop reusable AI components, services, APIs, prompts, workflows, and integration patterns that support enterprise scalability.\n, • Implement appropriate controls for security, privacy, traceability, model governance, monitoring, and responsible AI.\n, • Establish testing and evaluation approaches for AI solutions, including retrieval quality, hallucination reduction, response accuracy, reliability, latency, and overall solution performance.\n, • Support deployment, troubleshooting, performance optimization, and ongoing enhancement of production AI applications.\n, • Document architecture, technical designs, workflows, APIs, development standards, and operational procedures.\n, • Provide technical leadership and guidance to development teams while remaining actively involved in hands-on solution delivery.\n\n \n Required Qualifications \n\n • 10+ years of professional experience delivering technology, analytics, software engineering, AI, or enterprise application solutions across multiple business domains.\n, • Demonstrated hands-on experience delivering Generative AI and Agentic AI solutions in enterprise environments.\n, • Strong understanding of enterprise AI architecture and modern LLM application patterns.\n, • Proven experience designing and implementing multi-agent architectures and agent-based workflows.\n, • Hands-on experience with Retrieval-Augmented Generation, RAG, enterprise search, embeddings, semantic retrieval, and vector databases.\n, • Strong experience with AI orchestration frameworks such as:\n, • LangChain\n, • LangGraph\n, • CrewAI\n, • Model Context Protocol, MCP\n, • Experience developing AI solutions using cloud platforms such as AWS and/or Microsoft Azure.\n, • Experience integrating vector databases, enterprise data sources, APIs, cloud services, and LLM platforms.\n, • Strong programming experience, preferably with Python, and familiarity with modern software development practices.\n, • Experience with NLP technologies and architectures supporting conversational AI, text analysis, classification, summarization, extraction, or knowledge management.\n, • Experience designing or implementing forecasting and recommendation solutions.\n, • Strong understanding of APIs, microservices, data pipelines, application integration, and distributed system concepts.\n, • Demonstrated ability to convert business use cases into scalable technical solutions.\n, • Experience taking AI capabilities from proof of concept through production deployment.\n, • Strong analytical, problem-solving, communication, and stakeholder-management skills.\n, • Ability to work independently with minimal supervision while collaborating effectively across multidisciplinary teams.\n\n \n Preferred Qualifications \n\n • Experience implementing AI solutions in highly regulated or complex enterprise environments.\n, • Experience with enterprise AI governance, responsible AI, data privacy, security, and model monitoring.\n, • Familiarity with LLM evaluation frameworks, observability tools, prompt management, and AI performance monitoring.\n, • Experience developing reusable AI platforms, accelerators, frameworks, or shared enterprise services.\n, • Familiarity with CI/CD, DevOps, MLOps, or LLMOps practices.\n, • Experience working with structured and unstructured enterprise data.\n, • Knowledge of knowledge graphs, hybrid search, semantic search, or advanced retrieval techniques.\n, • Experience supporting AI-enabled workflow automation and human-in-the-loop processes.\n\n