AI Architect

Tata Consultancy Services; Pune District; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

Tata Consultancy Services seeks an experienced AI Architect to define enterprise AI architecture vision, design secure scalable AI solutions, and establish integration patterns across cloud and hybrid environments.

You will work with product owners, data scientists, engineers, and security teams to implement MLOps, ensure reproducibility, and guide selection of AI frameworks and cloud services on Google Cloud Platform.

Qualifications

• 7+ years of experience as an AI architect or similar leadership role.
• Hands-on with LLMs, prompt engineering, tool/function calling, memory management.
• Experience designing agent architectures and multi-agent orchestration patterns.
• Experience with cloud hyperscale platforms, preferably Google Cloud.
• Knowledge of MLOps/LLMOps, CI/CD, governance and reproducibility.

Responsibilities

• Define enterprise AI architecture vision and reference patterns aligned to goals.
• Design secure, scalable AI solutions across data ingestion, training, inference and feedback.
• Establish integration patterns to embed AI into existing platforms with clear boundaries.
• Define guidelines, reusable components and long-term AI roadmaps.
• Implement MLOps/LLMOps pipelines with versioning and approvals.
• Collaborate with product owners, engineers, security teams and stakeholders.
• Enforce least privilege IAM and zero-trust access controls.
• Operationalise observability and build telemetry dashboards and alerts.

Skills

Enterprise AI architecture

LLM integration

MLOps/LLMOps

Google Cloud Platform

Prompt engineering

APIs & microservices

Data pipelines

Security & IAM

Observability & telemetry

Agentic frameworks

Tools

LangGraph

Google ADK

MCP

A2A patterns

Job description

Role & responsibilities

Your responsibilities:

• Define the enterprise AI architecture vision and reference patterns; align them to business goals, risk posture, and engineering standards across cloud and hybrid environments.
• Design secure, scalable AI solutions covering data ingestion, feature engineering, model training, inference, and continuous feedback loops.
• Establish integration patterns (APIs, events, microservices) to embed model-powered capabilities into existing platforms with clear service boundaries.
• Define enterprise-wide AI architecture guidelines, reusable components, and long-term roadmap to ensure consistency and acceleration of AI initiatives.
• Implement MLOps/LLMOps pipelines for versioning, CI/CD, approvals, and controlled promotion across environments; enforce reproducibility.
• Work closely with product owners, data scientists, engineers, security teams, and business stakeholders to ensure architecture translates into high-value solutions.
• Enforce IAM least-privilege with IAM Conditions, organisation policies, and scoped service accounts; integrate BeyondCorp for zero-trust access.
• Operationalise observability using Cloud Logging, Cloud Monitoring, Error Reporting, Trace, and Profiler; build model/LLM telemetry dashboards and alerts.
• Identify the right AI/ML frameworks, cloud services, model orchestration tools, and infrastructure components that align with business needs and scalability goals
• Architect APIs, microservices, and integration patterns that embed AI capabilities seamlessly into existing workflows and digital products

Your Profile

Essential skills/knowledge/experience:

AI Architect: ( Exp Range 7+)

• Design agentic AI architectures using multi-agent orchestration patterns (planner-executor, supervisor-worker, tool-using agents).
• Define reference architectures for enterprise agent platforms integrating LLMs with systems of record (core banking, CRM, risk, payments).
• Design audit-ready agent interactions, tool usage logs, and decision provenance.
• Select and standardize frameworks (e.g., LangGraph, Google ADK, MCP, A2A patterns).
• Hands-on expertise with agentic frameworks (orchestrators).
• Experience with LLMs, prompt engineering, tool/function calling, memory management.
• API-first integration, event-driven architectures, and data pipelines.
• Exposure to AI quality metrics: task success rate, groundedness, containment, FCR.
• Experience on Google Cloud Platform (preferred) or equivalent hyperscale.
• Deep understanding of LLMs, generative AI, RAG patterns, vector databases, embeddings, and prompt/guardrail engineering.