Gen AI Technical Lead

Tata Consultancy Services; India; On-site; INR 3,500,000 - 5,500,000; Full time

Job summary

Tata Consultancy Services is seeking an experienced GenAI Architect to lead the design and delivery of Generative AI workflows and scalable microservices for enterprise applications.

You will mentor a small team, integrate LLMs across systems, and work with cloud providers to ensure secure, compliant deployments. Offshore centers welcome applicants with strong Python and framework experience.

Qualifications

• Minimum 7-10 years of total IT experience with at least 2+ years in Generative AI/LLM tech.

Responsibilities

• Design, architect, and implement Generative AI workflows and agents using LangChain, LangGraph, or Google ADK.
• Integrate LLMs into enterprise systems and custom applications.
• Define and implement retrieval-augmented generation pipelines using vector databases.
• Architect scalable and secure GenAI microservices on cloud-native components.
• Lead Python-based development for prompt orchestration, tool agents, and data pipelines.
• Develop and deploy APIs or microservices integrating LLMs with enterprise data sources.
• Implement prompt optimization, context management, and model performance tuning.
• Lead a small team of AI engineers and mentors; collaborate with product owners and stakeholders.

Skills

Python

GenAI frameworks

LLM integration

RAG/embeddings

Cloud hyperscalers

MLOps/DevOps

Team mentoring

Solution design

Tools

LangChain

LangGraph

Google ADK

OpenAI API

Gemini API

Pinecone

FAISS

Vertex AI

BigQuery

Job description

Experience Level: Minimum 7-10 years of total IT experience (at least 2+ years in Generative AI, LLMs, or related AI/ML technologies)

• Should have strong technical expertise in Python, hands-on experience with at least one GenAI framework (LangGraph, LangChain, or Google AI Development Kit), and strong working knowledge of one hyperscaler platform (Google Cloud, Azure, or AWS).
• The associate should lead solution design, integrating LLMs into enterprise workflows, mentoring team members, and driving production-grade implementation of GenAI use cases.
• Good knowledge of MLOps or DevOps teams to automate model deployment, versioning, and monitoring.

Key Responsibilities:

1. Solution Design

• Design, architect, and implement Generative AI workflows and agents using frameworks such as LangChain, LangGraph, or Google ADK.
• Integrate LLMs (e.g., Llama, Gemini, GPT, Claude) into enterprise systems and custom applications.
• Define and implement retrieval-augmented generation (RAG) pipelines using vector databases (e.g., ChromaDB, Pinecone, FAISS, Weaviate, Vertex AI Matching Engine).
• Architect scalable and secure GenAI microservices leveraging cloud-native components.

2. Development & Implementation

• Lead Python-based development efforts for building prompt orchestration, tool agents, and data pipelines.
• Develop and deploy APIs or microservices integrating LLMs with enterprise data sources.
• Implement prompt optimization, context management, and model performance tuning.

3. Cloud Integration

• Architect, deploy, and monitor GenAI workloads on one hyperscaler:
  • GCP (Vertex AI, Document AI, AlloyDB, BigQuery, Cloud Run)
  • Azure (OpenAI Service, Cognitive Search, Azure ML)
  • AWS (Bedrock, SageMaker, Lambda, API Gateway)
• Manage cloud infrastructure for scaling AI models, ensuring cost efficiency and compliance.

4. Leadership & Mentoring

• Lead a small team of AI engineers and developers.
• Conduct code reviews, enforce best practices, and mentor junior engineers.
• Collaborate closely with product owners, data engineers, and business stakeholders to translate business needs into technical requirements.
• Contribute to internal GenAI capability building and reusable assets for the organization.

Required Skills & Experience:

1. Core Technical Skills

• Python (advanced proficiency; ability to build APIs, pipelines, and modular frameworks).
• Hands-on with at least one GenAI framework:
• LangChain, LangGraph, or Google ADK (AI Development Kit).
• Expertise with LLM integration (OpenAI API, Gemini API, Ollama, Hugging Face, etc.).
• Experience with RAG, embeddings, and vector databases.
• Familiarity with PEFT, LoRA, or prompt fine-tuning approaches.
• Cloud / Hyperscaler Expertise (at least one required)
• Google Cloud Platform (GCP) Vertex AI, Document AI, BigQuery, AlloyDB, Cloud Run, IAM
• Azure – Azure OpenAI, Cognitive Search, Azure ML, Azure Functions
• AWS – Bedrock, SageMaker, Lambda, API Gateway, DynamoDB

2. Other Desirable Skills

• Knowledge of REST APIs, JSON, and FastAPI/Flask frameworks.
• Familiarity with data governance, PII handling, and AI ethics principles.
• Understanding of Docker/Kubernetes, CI/CD, and Git-based version control.
• Exposure to front-end integration with AI chat agents (React, Streamlit, Gradio, etc.) is a plus.

3. Location:

• Offshore, open to all TCS ODC located areas