Gen AI Lead

Tata Consultancy Services; Irving (TX); On-site; USD 80,000 - 125,000; Full time

Job summary

Tata Consultancy Services in Irving, TX, is hiring a GenAI Engineer to own end-to-end delivery of AI-powered applications. You will design, test, deploy, and monitor GenAI solutions in cloud environments, collaborating with cross-functional teams to meet enterprise needs.

The role emphasizes building autonomous, AI-driven workflows, integrating advanced retrieval systems, and ensuring governance and security. You will optimize latency, cost, and reliability while mentoring junior engineers.

Qualifications

• Expert-level proficiency in Python; other languages as needed.
• Experience with LangChain, LlamaIndex, LangGraph, CrewAI, or AutoGen.
• Experience with vector databases, hybrid search, document chunking, knowledge graphs (Neo4j).
• Cloud and MLOps: Docker, Kubernetes, CI/CD, and cloud platforms (AWS, GCP, Azure).
• Observability: tools for monitoring model drift and output quality.

Responsibilities

• End-to-End Delivery: Own the full lifecycle of GenAI applications from design to cloud deployment and monitoring.
• Agentic Workflows: Build autonomous AI agents that plan, reason, and execute multi-step workflows using tools and APIs.
• RAG & Knowledge Integration: Implement retrieval systems with vector DBs and Graph RAG to ground AI in enterprise data.
• System Optimization: Tune latency, throughput, cost, and hardware utilization for large-scale LLM inference.
• Governance & Safety: Enforce responsible AI standards, privacy, testing, evaluation tracing, and guardrails.
• Leadership: Mentor junior engineers and drive architectural best practices.

Skills

Python

Go

Java

C++

LangChain

LlamaIndex

LangGraph

CrewAI

AutoGen

Vector databases

Neo4j

Docker

Kubernetes

AWS

GCP

Azure

CI/CD

Monitoring

Model evaluation

Education

Bachelor's degree in Computer Science

Tools

Docker

Kubernetes

Neo4j

CI/CD pipelines

Job description

Job Description

Must Have Technical/Functional Skills

Programming: Expert-level proficiency in Python (plus Go, Java, or C++ as needed).

AI Frameworks: Hands-on experience with LangChain, LlamaIndex, LangGraph, CrewAI, or AutoGen.

Data & Retrieval: Vector databases, hybrid search, document chunking, and knowledge graphs (e.g., Neo4j).

Cloud & MLOps: Containerization with Docker and Kubernetes, CI/CD pipelines, and cloud platforms like AWS, GCP, or Azure.

Observability: Production evaluation and tracing tools for monitoring model drift and output quality.

Roles & Responsibilities

• End-to-End Delivery: Own the full lifecycle of GenAI applications from initial design and testing to cloud deployment and monitoring. [1, 2]
• Agentic Workflows: Build autonomous, goal-driven AI agents that can plan, reason, and execute multi-step workflows using tool-calling and APIs. [1]
• RAG & Knowledge Integration: Implement advanced retrieval systems, vector databases, and Graph RAG to ground AI responses in enterprise data and prevent hallucinations. [1, 2]
• System Optimization: Tune latency, throughput, cost-efficiency, and hardware utilization for large-scale LLM inference. [1]
• Governance & Safety: Enforce responsible AI standards, including data privacy compliance, automated testing, evaluation tracing, and security guardrails. [1, 2]
• Leadership: Mentor junior engineers, drive architectural best practices, and bridge the gap between technical teams and business stakeholders.

Salary Range: $80,000 to $125,000 per year

Qualifications: BACHELOR OF COMPUTER SCIENCE