AI & Data Architect

Role Summary

The AI & Data Architect is responsible for defining and governing the enterprise architecture for data, analytics, and AI platforms, ensuring scalable, secure, and compliant AI‑driven solutions. The role bridges business strategy, data platforms, and AI/ML engineering, enabling organizations to turn data into trusted intelligence and measurable business outcomes.

Key Responsibilities

Architecture & Strategy

• Define end‑to‑end AI & Data architecture across ingestion, storage, processing, analytics, and AI/ML layers.
• Establish enterprise data and AI architecture standards, patterns, and reference architectures.
• Align AI & Data strategy with business objectives, cloud strategy, and technology roadmaps.

Data Platform & Engineering

• Design modern data platforms (lakehouse, data mesh, data fabric) to support batch, streaming, and real‑time analytics.
• Guide data ingestion, transformation, metadata, lineage, and data quality strategies.
• Ensure interoperability across source systems, analytics platforms, and AI workloads.

AI / ML Enablement

• Architect AI/ML and GenAI solutions including model lifecycle, MLOps/LLMOps, and deployment patterns.
• Support scalable integration of predictive analytics, ML models, and GenAI capabilities into business applications.
• Ensure governable, explainable, and production‑ready AI solutions.

Governance, Security & Compliance

• Embed data governance, privacy, security, and responsible AI principles into architecture designs.
• Ensure compliance with regulatory, risk, and enterprise security standards.
• Define observability, cost optimization, and resilience for AI & data platforms.

Stakeholder Leadership

• Act as a trusted advisor to business leaders, product teams, engineering teams, and vendors.
• Lead architecture reviews, design trade‑offs, and technology selection decisions.
• Mentor teams and promote architectural best practices across programs.

Required Skills & Experience

• 10+ years of experience in data, analytics, and architecture roles, with strong AI/ML exposure.
• Deep understanding of:
  • Data architectures (Lakehouse, Data Mesh/Fabric)
  • AI/ML & GenAI concepts and production patterns
  • Cloud platforms (AWS / Azure / GCP)
• Strong experience in enterprise‑scale architecture, system integration, and platform design.
• Ability to translate business vision into scalable technical architectures.

Preferred Qualifications

• Experience in regulated industries (Banking, Financial Services, Insurance).
• Exposure to AI governance, model risk, and responsible AI frameworks.
• Architecture certifications (TOGAF, Cloud Architect, AI/Data certifications).