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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Solution Architect - Ai - **Company:** Tata Consultancy Services Limited - **Location:** Edison, NJ, United States - **Experience:** Experienced - **Salary:** $120,000 - $150,000 - **Contract:** Permanent contract - **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Application Frameworks, Automation of Tests, Microsoft Azure, Computer Programming, Continuous Integration, Data as a Services, Information Extraction, Python (Programming Language), Machine Learning, Metadata, NoSQL, Microsoft Copilot, Software Deployment, Data Streaming, Systems Integration, Management of Software Versions, Enterprise Search, Google Cloud, Chatbots, Prompt Libraries, Retrieval-Augmented Generation, Delivery Pipeline, Large Language Models, Prompt Engineering, Generative AI, Langfuse — LLM Observability and Analytics Platform, Agentic-AI, Fastapi, Build Management, Pytest, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Low Latency, Data Management, Machine Learning Operations, Evaluation of Large Language Models, Graph RAG, Serverless Computing, Human in the Loop, Docker, Microservices, Agent2Agent Protocol - **Published:** November 7, 2026 - **Apply:** https://jobs.localjobnetwork.com/apply/add/88810096/1 ## About the Role * 3+ years of hands-on experience designing and delivering AI, machine learning or Generative AI solutions, including production deployment. * Advanced, recent experience writing production-grade Python using async programming, typing, Pydantic, packaging, pytest and FastAPI. * Hands-on experience building agentic systems with LangGraph and LangChain, including stateful graphs, tools, memory, checkpointing, structured outputs and human-in-the-loop controls. * Experience using Langfuse or an equivalent platform for tracing, prompt management, evaluation, datasets, feedback and cost analytics. * Experience building and consuming MCP servers and tools, with familiarity with A2A protocols and at least one additional agent framework. * Demonstrated architecture experience with AWS, Microsoft Azure or Google Cloud Platform, including relevant Generative AI, integration, security, monitoring and deployment services. * Hands-on experience with LLMs, prompt engineering, RAG, embeddings, semantic and hybrid search, reranking, grounding, citations and vector stores. GraphRAG and knowledge-graph experience is preferred. * Experience designing secure APIs, microservices, asynchronous or event-driven integrations, and solutions using relational, NoSQL, object, search or vector data platforms. * Hands-on knowledge of Docker, Kubernetes, serverless deployment, Infrastructure as Code, CI/CD, automated testing and LLM evaluation gates. * Demonstrated ability to define secure, scalable and observable architectures and communicate technical decisions, risks and trade-offs to technical and executive stakeholders., BACHELOR OF COMPUTER SCIENCE ## Description The Generative AI Architect will lead the design and hands-on delivery of secure, scalable, production-ready Generative AI solutions on AWS or Azure. The role combines architecture leadership with production-grade Python engineering across agentic workflows, RAG, MCP integrations, evaluation, observability, Responsible AI and cloud-native deployment. Approximately 40-50% of the role involves hands-on engineering, including proofs of concept, reusable frameworks and deployment pipelines., * Lead discovery workshops and assess Generative AI use cases based on business value, technical feasibility, data readiness, security risk, complexity and operating cost. * Define high-level and detailed architectures for Generative AI applications on AWS, Azure or GCP, including conversational AI, enterprise search, document intelligence, copilots, content generation and workflow automation. * Select appropriate foundation and embedding models, orchestration frameworks, vector stores, data services and deployment components. * Build production-grade agentic workflows in Python using LangGraph and LangChain, including routing, tools, memory, checkpointing, persistence, streaming, structured outputs and human-in-the-loop controls. * Develop reusable agent frameworks, connectors, MCP servers and clients, prompt libraries, evaluation harnesses and other accelerators for delivery teams. * Design and build RAG and GraphRAG solutions covering ingestion, chunking, metadata, embeddings, hybrid search, reranking, grounding, citations and res ponse generation. *Implement LLM observability and evaluation using Langfuse or equivalent tools, including tracing, prompt versioning, feedback, quality metrics, token usage, latency and cost monitoring. * Build secure agent services with FastAPI and async Python, containerize them with Docker, and deploy them using Kubernetes, serverless or managed cloud platforms. * Establish Responsible AI, security, LLMOps and GenAIOps controls, including guardrails, automated evaluation gates, CI/CD, governance and operational monitoring. * Lead delivery from proof of concept through production stabilization, producing architecture documentation, reviewing code, resolving technical risks, mentoring teams and communicating decisions to stakeholders.