Gen AI Architect

Tata Consultancy Services; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Tata Consultancy Services invites an experienced Generative AI Architect to design and implement scalable AI workflows across GCP, Azure, and AWS. You will lead cross‑functional teams, shape the technical vision, and ensure alignment with business objectives.

The role focuses on integrating LLMs, building robust RAG pipelines, containerized deployments, and strong governance, security, and ethics, while mentoring engineers and collaborating with product stakeholders.

Qualifications

• 10+ years IT experience with 2+ years in generative AI
• Advanced Python proficiency for APIs, pipelines
• Hands-on with at least one generative AI framework
• Strong knowledge of RAG, embeddings, vector databases
• Familiar with PEFT/LoRA or prompt tuning
• Cloud expertise with at least one hyperscaler (GCP/Azure/AWS)

Responsibilities

• Design, architect, and implement generative AI workflows and agents
• Integrate LLMs into enterprise systems
• Develop and optimize retrieval-augmented generation pipelines
• Lead multi-cloud architecture and secure deployments
• Mentor AI engineers and guide best practices
• Governance, security, and ethics in AI initiatives
• Collaborate with product stakeholders to translate requirements
• Monitor production AI performance and risks

Skills

Python

LangChain / LangGraph

LLM integration

Cloud platforms (GCP, Azure, AWS)

Docker & Kubernetes

REST APIs / FastAPI / Flask

MLOps & CI/CD

Education

Bachelor’s or Master’s in Computer Science / AI

Tools

LangChain

LangGraph

Google ADK

Azure OpenAI Studio

AWS Bedrock

SageMaker

Job description

The Generative AI Architect is a strategic technical leader responsible for designing, implementing, and managing advanced generative AI solutions across Google Cloud Platform (GCP), Microsoft Azure, and Amazon Web Services (AWS). This role combines deep expertise in generative AI technologies, multi-cloud architecture, and modern software engineering practices to deliver scalable, secure, and innovative AI products. The architect leads cross-functional teams, drives technical vision, and ensures alignment between business objectives and AI initiatives.

Key Responsibilities

• Design, architect, and implement generative AI workflows and agents using frameworks like LangChain, LangGraph, Google AI Development Kit (ADK), Azure AI Studio or AWS Bedrock.
• Integrate large language models (LLMs) such as Llama, Gemini, GPT, and Claude into enterprise systems and custom applications.
• Develop and optimize retrieval-augmented generation (RAG) pipelines using vector databases (ChromaDB, Pinecone, FAISS, Weaviate, Vertex AI Matching Engine).
• Supervise the design, fine-tuning, and optimization of generative AI models and multimodal systems (text, image, audio).
• Lead Python-based development for prompt orchestration, tool agents, APIs, and data pipelines.
• Architect, deploy, and monitor generative AI solutions on GCP (Vertex AI, Document AI, AlloyDB, BigQuery, Cloud Run), Azure (OpenAI Service, Cognitive Search, Azure ML, Azure Functions), and AWS (Bedrock, SageMaker, Lambda, API Gateway, DynamoDB).
• Design and manage scalable cloud infrastructure, ensuring performance, cost efficiency, and compliance across platforms.
• Implement containerization and orchestration strategies using Docker and Kubernetes (GKE/EKS/AKS) for reliable deployment.
• Establish and enforce security frameworks using GCP IAM, Azure Identity, AWS IAM, and related tools for secure, compliant access.
• Utilize monitoring and logging solutions such as Google Cloud Operations Suite, Azure Monitor, and AWS CloudWatch.

MLOps, DevOps & Governance

• Automate model deployment, versioning, and monitoring using MLOps/DevOps best practices and CI/CD pipelines.
• Implement prompt optimization, context management, and model performance tuning.
• Ensure adherence to data governance, privacy, PII handling, and AI ethics principles throughout the development lifecycle.
• Lead and mentor teams of AI engineers and developers, fostering a culture of collaboration and innovation.
• Conduct code reviews, enforce best practices, and guide junior engineers.
• Partner with product owners, data engineers, and business stakeholders to translate requirements into effective AI solutions.
• Communicate technical concepts, project status, and business impact to both technical and non-technical audiences, including executive leadership.
• Drive research and innovation, staying current with advancements in generative AI, multimodal models, and AI agents.
• Contribute to internal capability building and development of reusable AI assets for the organization.
• Monitor AI model performance in production, manage risks, and drive continuous improvement initiatives.

Required Skills & Experience

• 10+ years of IT experience, with 2+ years in generative AI, LLMs, or related AI/ML technologies.
• Advanced proficiency in Python, with ability to build modular APIs, pipelines, and orchestration frameworks.
• Hands-on expertise in at least one generative AI framework: LangChain, LangGraph, Google ADK, Azure AI Studio or AWS Bedrock and understanding of others.
• Strong knowledge of RAG, embeddings, and vector databases.
• Familiarity with PEFT, LoRA, or prompt fine-tuning approaches.
• Cloud expertise in at least one hyperscaler (GCP, Azure, or AWS), with working knowledge of:
• AWS: Bedrock, SageMaker, Lambda, API Gateway, DynamoDB, IAM
• Understanding of Docker/Kubernetes, CI/CD, and Git-based version control.
• Knowledge of REST APIs, JSON, and frameworks like FastAPI or Flask.
• Familiarity with data governance, security, and AI ethics.
• Strong problem-solving, project management, and communication skills.
• Ability to drive innovation and influence strategic decisions.

Education

• Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or a related field.

Nice to Have

• Experience with AI agents and multimodal AI models (text, image, audio).
• Exposure to front-end integration with AI chat agents (e.g., React, Streamlit, Gradio).

Location

• Open to all locations, including offshore and onshore, with flexibility to work with distributed teams.