Data Architect (D)

This is a 6 – month contract with our client in the Fintech industry

Our client is seeking an AI Data Architectto design, govern, and evolve their end-to-end enterprise artificial intelligence landscape. In this strategic role, you will bridge the gap between business vision and advanced technology—ensuring that machine learning, GenAI, and intelligent automation platforms are scalable, production-grade, and seamlessly integrated into the core cloud and data ecosystems.

As the technical authority for AI, you will champion a "responsible-by-design" approach, embedding ethics, security, and governance into every blueprint.

Responsibilities include

• Enterprise Blueprinting: Define and own the end-to-end AI architecture. Translate product strategies into production-grade solution designs covering data ingestion, feature engineering, model serving, and API integrations.
• Platform & Core Capabilities:Architect scalable platforms supporting advanced MLOps and LLMOps capabilities, including CI/CD pipelines, observability, drift detection, and automated retraining.
• Responsible AI Governance: Establish robust frameworks for model risk management, evaluation, fairness, transparency, and regulatory compliance throughout the full AI lifecycle.
• Technical Leadership: Act as the ultimate subject matter expert for AI initiatives, mentoring data science and engineering teams while driving enterprise-wide AI transformation.
• Strategic Change Sign-off: Lead strategy roadmaps, propose SLA policies, manage process escalations, and provide architectural sign-off for changes impacting the data and AI environments.

Requirements include:

• Education: Bachelor's degree in Computer Science, Information Systems, Data Management, or a related technical field. Professional certifications are a distinct advantage.
• Experience: 5+ years in AI, data, or solution architecture roles with a proven track record of delivering enterprise-grade AI solutions in large, regulated environments.
• Industry Edge: Strong preference for Financial Services, Banking, or Telecommunications experience—ideally with exposure to Mobile Money and cross-geography operations.
• Technical Mastery: Deep conceptual and practical understanding of cloud data platforms, big data ecosystems, structured/unstructured data processing, LLMs, AI agents, RAG patterns, and orchestration.

Originally posted on Himalayas