GenAI Solution Architect

About the Role

seasoned AI / GenAI Solution Architect

Key Responsibilities

• Architect and deliver

  end-to-end GenAI, LLM, and Agentic AI solutions

  for enterprise clients.
• Design

  scalable, secure, and cost-effective AI-ready architectures

  leveraging cloud technologies (Azure preferred).
• Provide technical leadership in

  GenAI framework design

  , MLOps, and LLMOps implementations.
• Develop

  AI CoPilots, chatbots, and agentic workflows

  using modern frameworks and APIs.
• Lead

  proposal creation, estimations, and RFP/RFI responses

  for AI/GenAI engagements.
• Collaborate with clients as a

  trusted AI advisor

  , driving solution roadmaps and transformation initiatives.
• Build

  reference architectures, reusable components, and accelerators

  for GenAI adoption.
• Collaborate cross-functionally with

  data engineering, product, and cloud teams

  to ensure successful delivery.

Required Qualifications & Skills

Experience:

• 15+ years

  of overall IT experience, with

  5+ years as an AI/ML/GenAI Architect.

Technical Expertise:

• Strong hands-on expertise in

  Python

  for AI/ML development.
• Proven experience designing and implementing

  LLM and Agentic AI architectures.

• Deep understanding of

  GenAI frameworks, MLOps, and LLMOps.

• Strong exposure to

  data engineering pipelines

  (ETL, data lakes, orchestration).
• Hands-on with

  Azure AI stack

  (Azure OpenAI, Cognitive Services, CoPilot, AI Studio).
• Additional experience with

  GCP or AWS AI/ML services

  preferred.
• Familiarity with

  Databricks, Snowflake, Palantir, or similar data platforms.

Client & Leadership Skills:

• Proven success in

  client-facing roles

  , handling

  RFPs, proposals, and estimations.

• Excellent

  stakeholder management, communication, and leadership

  abilities.
• Ability to mentor teams and build

  GenAI solution accelerators

  and PoCs.

Preferred Certifications:

• Azure AI Engineer / Azure Solution Architect / GCP ML Engineer / AWS ML Specialist

  certifications are an advantage.

Domain Exposure (Preferred):

• Experience in

  BFSI, Healthcare, Manufacturing, or Public Sector

  with

  regulatory-compliant AI systems.

Key Highlights

• Hands-on

  Python, GenAI, LLM, and Agentic architecture

  experience
• Strong

  Azure and CoPilot

  expertise
• Ability to

  build

  (not just conceptualize) GenAI and agentic frameworks
• Deep understanding of

  AI transformation programs

  at enterprise scale