Generative AI /AI/ Solution AI Architect

Role Overview: (10-16 Years)

We are seeking a hands-on, strategic Generative AI Solution Architect to lead GenAI initiative for our customers. This role is ideal for an expert with deep knowledge of enterprise GenAI architectures with strong security governance frameworks, data integration, and tooling ecosystems. The selected architect will be responsible for driving the blueprint, high/low-level designs, and implementation strategy for an enterprise-grade GenAI platform, with a strong focus on security, compliance, citizen development, and extensibility. The person should have hands-On expertise of building production grade GenAI solutions

Key Responsibilities:

Architecture & Design Leadership -

• Own the creation of the enterprise-wide GenAI Architecture Blueprint—addressing scalability,
• extensibility, performance, and modularity.
• Deliver comprehensive High-Level Design (HLD) and Low-Level Design (LLD), including Model
• orchestration, Integration patterns (APIs, vector DBs, prompt chaining),
• Tooling (e.g., LangChain, Semantic Kernel, LLMOps), Security flows
• Recommend optimal deployment models—single-cloud vs multi-cloud—and determine appropriate
• data hosting and data sharing strategies.

Security, Governance & Guardrails -

• Define and implement comprehensive security architecture for GenAI use, including RBAC, access
• controls, data privacy, and identity management (SSO, OAuth2 integrations).
• Establish responsible AI guardrails (bias mitigation, prompt safety, content moderation) and align them
• with RRA's legal/compliance expectations.
• Collaborate with the CISO to define risk management frameworks, auditability practices, and policies for
• model usage transparency.
• Integrate VPN tunnels and secure connectors for tools like Databricks, ensuring private data remains
• protected during AI operations.

Data & Platform Enablement -

• Design and align GenAI platform integration with existing data catalog, data lake, and metadata management strategy.
• Define secure and compliant workflows for data sharing, data retrieval, and residual risk mitigation.
• Recommend platform direction—e.g., whether use cases should run through centralized studio, embedded Copilot layers, or hybrid delivery modes.

Citizen Development & Enterprise Enablement -

• Define onboarding and enablement strategy for Citizen Developers using tools like Sana Labs, OpenAI
• Enterprise, Microsoft Copilot Studio
• Recommend governance layers to ensure safe usage of AI building blocks by non-technical users.

Tools, Stack & Model Providers -

• Assess and recommend LLM providers (e.g., OpenAI, Claude, Mistral, Cohere, Bedrock, Azure OpenAI).
• Evaluate toolchain options for: Prompt engineering, Model evaluation and observability, RAG pipelines,
• AI Agents, LLMOps
• Define model lifecycle management and provider switching strategies to reduce vendor lock-in.

Program Execution & Delivery

• Create a structured implementation roadmap, identifying milestones, dependencies, and KPIs.
• Participate in architecture review sessions with ARC/ARB
• Collaborate in weekly review meetings and a mid-engagement checkpoint to present interim designs and track risks.

Required Skills & Qualifications:

10+ years in enterprise architecture & consulting with recent focus on Generative AI / LLMs / AI

Governance

• Strong experience with cloud-native AI platforms (Azure, AWS, GCP) and security design patterns
• Hands-on with RAG architectures, AI Agents design patterns, prompt pipelines, vector DBs, and LLMOps

Toolchains

• Deep understanding of security frameworks required in Generative AI ecosystem (RBAC, zero trust, data tokenization, SSO etc)
• Familiarity with data platforms like Databricks, Snowflake, and secure data tunneling
• Excellent documentation, stakeholder engagement, and executive presentation skills

Nice to Have:

• Experience working directly with CISO organizations, data stewards, or compliance teams
• Prior work with AI-enablement platforms like Copilot Studio, Sana Labs, and enterprise OpenAI deployments
• Familiarity with audit and residual risk mitigation practices in AI/ML systems

Key Deliverables:

• Enterprise GenAI Architecture Blueprint
• Security & Governance Framework for GenAI
• Use Case Feasibility & ROI Analysis
• HLD & LLD Documents (Tech, Security, Tooling, Data)
• Toolchain & Model Provider Recommendations
• Citizen Developer Enablement Guidelines
• Implementation Roadmap & Milestone Plan

Job Type: Full-time

Pay: ₹1,000,000.00 - ₹4,500,000.00 per year

Work Location: In person