Gen AI Technical Architect

Gen AI Technical Architect (Hands on Implementation exp.)

Raritan, NJ (Hybrid)

We are seeking an experienced Gen AI Technical Architect with strong hands-on implementation expertise to design, develop, and deploy enterprise-grade Generative AI solutions. The ideal candidate has successfully delivered Gen AI applications into production environments, not just Proofs of Concept (POCs), and possesses deep technical expertise in Python, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI systems.

This role requires someone who can combine architecture with coding, drive solution design, build production-ready AI applications, and clearly articulate the reasoning behind Gen AI architectures and agent-based systems.

Required Experience:

• Hands-on Gen AI Architect, hybrid work 3-4 days in Raritan, NJ.

• Must have implemented Gen AI solutions in production, not just POCs.

Key Responsibilities

• Design, architect, and implement scalable enterprise Generative AI solutions.

• Build and deploy production-grade Gen AI applications using modern LLM frameworks and cloud technologies.

• Develop and showcase innovative Gen AI Proofs of Concept (POCs) that address business challenges.

• Design and implement Agentic AI systems, including autonomous workflows, planning, reasoning, memory, and tool orchestration.

• Develop robust Python-based AI applications and services with production-quality code.

• Design and implement Retrieval-Augmented Generation (RAG) architectures using vector databases and enterprise data sources.

• Collaborate with business stakeholders, product teams, and engineering teams to translate requirements into AI solutions.

• Evaluate foundation models and recommend the appropriate architecture based on use cases.

• Optimize AI solutions for scalability, security, latency, and cost.

• Mentor engineering teams on Gen AI best practices, architecture patterns, and implementation strategies.

Required Technical Skills

• Python

• Large Language Models (OpenAI, Anthropic, Llama, Gemini, etc.)

• LangChain, LangGraph, LlamaIndex, or similar orchestration frameworks

• Agentic AI frameworks and autonomous AI systems

• Retrieval-Augmented Generation (RAG)

• Vector databases (Pinecone, Weaviate, Chroma, Milvus, FAISS, etc.)

• Prompt Engineering

• AI orchestration and workflow automation

• REST APIs and microservices

• Git, CI/CD, Docker, Kubernetes

• Cloud platforms (AWS, Azure, or Google Cloud)