Description

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)

Key Skills

Education

Bachelor's degree