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# GenAI Architect

- **Company:** Tata Consultancy Services Limited
- **Location:** Ewing Township, NJ, United States
- **Salary:** $190,000.0 - $220,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Software Applications, Microsoft Azure, Information Engineering, Extract Transform Load (ETL), Python (Programming Language), TypeScript, Web Services, Large Language Models, Kubernetes, Information Technology, HuggingFace, Machine Learning Operations, Api Design, Serverless Computing, Docker, Microservices
- **Published:** June 20, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=d605db7926af4128

## About the Role

Do you have experience in Web services design?, Do you have a Bachelor's degree?, Qualifications : BACHELOR OF COMPUTER SCIENCE

## Description

* Programming: Python (primary), familiarity with TypeScript/Node.js for service integration

 * Frameworks: LangChain, LlamaIndex, LangGraph, AutoGen (agent orchestration)

 * Vector databases: Pinecone, Weaviate, Milvus, pgvector, Chroma

 * LLM/API platforms: OpenAI, Anthropic Claude, Azure OpenAI, AWS Bedrock, Google Vertex AI, Hugging Face

 * Fine-tuning techniques: LoRA, QLoRA, PEFT, RLHF fundamentals

 * Infrastructure: Docker, Kubernetes, serverless deployment on Azure/AWS/GCP

 * MLOps/LLMOps tooling: MLflow, Weights & Biases, evaluation/observability frameworks (e.g., LangSmith, Arize)

 * Data engineering: ETL pipelines, embeddings generation, knowledge graph integration

 * API design and microservices architecture for AI-powered applications

Roles & Responsibilities

 * Define enterprise Gen AI strategy, reference architecture, and technology standards

 * Evaluate, select, and benchmark LLM providers and foundation models for specific use cases

 * Architect RAG pipelines, agentic workflows, and multi-agent orchestration systems

 * Establish responsible AI practices: guardrails, content filtering, bias and hallucination mitigation

 * Lead Gen AI solutions from proof-of-concept through production-grade deployment

 * Partner with data science, engineering, and business teams to prioritize and scope use cases

 * Monitor model performance, cost, latency, and drift in production environments

 * Mentor engineering teams on Gen AI best practices and emerging techniques

 * Stay current with the rapidly evolving Gen AI landscape and advise leadership on adoption roadmap


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