AI Architect

The AI Architect is responsible for defining designing and governing endtoend AI and GenAI solutions that are scalable secure and aligned with business strategy. This role bridges business needs data engineering ML engineering and cloud platforms to deliver productiongrade AI systems across the enterprise.

Technical Skills

• Strong experience in AI architecture machine learning and deep learning concepts.
• Handson knowledge of Python and ML frameworks (TensorFlow PyTorch Scikitlearn).
• Experience with GenAI and LLM ecosystems (OpenAI Azure OpenAI Anthropic opensource models).
• Expertise in MLOps tools and practices (CI/CD model registries monitoring).
• Experience with data platforms (Data Lakes Snowflake BigQuery Databricks).
• Knowledge of APIbased AI integration MCP microservices and eventdriven architectures.

Cloud & DevOps

• Strong experience with at least one major cloud platform: Azure AWS or GCP.
• Familiarity with containerization and orchestration (Docker Kubernetes).
• Understanding of infrastructureascode and DevSecOps practices.

Experience

• 812 years of overall IT experience with 35 years in AI/ML solution or platform architecture roles.
• Proven experience delivering AI solutions into production at scale.

AI Strategy & Architecture

• Define enterprise AI/ML and GenAI architecture roadmaps aligned with business objectives.
• Design scalable reusable and secure AI solution architectures (traditional ML deep learning LLMbased systems).
• Evaluate and select AI platforms frameworks and cloud services.

Solution Design & Delivery

• Architect endtoend AI solutions encompassing data ingestion feature engineering model training inference monitoring and feedback loops.
• Lead architecture for use cases such as predictive analytics NLP computer vision recommendation systems and AI agents.
• Design architectures leveraging LLMs vector databases RAG frameworks finetuning and prompt engineering.

Governance Security & Compliance

• Define AI governance standards including model lifecycle management data privacy bias mitigation explainability and responsible AI.
• Ensure compliance with enterprise security regulatory and ethical AI standards.
• Establish best practices for model versioning monitoring and drift management.

Stakeholder & Technical Leadership

• Act as a trusted advisor to business leaders product owners and delivery teams on AI feasibility and value realization.
• Translate business problems into AI solution designs and technical blueprints.
• Mentor data scientists ML engineers and architects; review solution designs and implementations.

• Experience designing AI solutions in regulated industries (Insurance or BFSI)
• Knowledge of Responsible AI explainable AI (XAI).
• Experience with AI agent frameworks and autonomous workflows.
• Strong architectural thinking and problemsolving skills
• Ability to balance innovation with enterprise governance
• Excellent stakeholder communication and technical storytelling
• Leadership and mentoring mindset