AI ML Architect

NOTICE PERIOD: - 15-20 DAYS ONLY

Role Summary: We are seeking an AI Architect to lead strategic AI transformation initiatives within the Banking, Financial Services, and Insurance (BFSI) domain. This role demands deep hands-on experience in AI, Machine Learning (ML), and Generative AI (GenAI), along with the ability to engage directly with C-level stakeholders, align technical delivery with business objectives, and drive enterprise-wide adoption of advanced AI solutions. The ideal candidate is a techno-strategic leader who can take AI/ML/GenAI projects from ideation to production—building architectures, leading cross-functional teams, and ensuring regulatory and operational alignment in BFSI environments.

Key Responsibilities: Consulting & Business Alignment • Partner with senior business and IT leadership, including CIOs, CDOs, and COOs, to identify high-impact use cases across retail banking, insurance, credit, and capital markets. • Translate complex BFSI challenges into technically feasible and scalable AI/ML/GenAI solutions. • Create strategic roadmaps, capability assessments, and PoV/PoC execution plans that align with business KPIs and regulatory needs.

Solution Architecture & Delivery Leadership

• Design and lead delivery of AI/ML/GenAI pipelines covering data ingestion, model training, validation, deployment, and monitoring. • Build and scale GenAI-based solutions like LLM-driven chatbots, intelligent document processing, RAG pipelines, summarization tools, and virtual assistants. • Architect cloud-native AI platforms using AWS (SageMaker, Bedrock), Azure (ML, OpenAI), or GCP (Vertex AI, BigQuery, LangChain). • Define and implement MLOps and LLMOps frameworks for versioning, retraining, CI/CD, and production observability. • Ensure adherence to Responsible AI principles, including explainability, bias mitigation, auditability, and compliance with BFSI regulations (GDPR, RBI, SOC2, etc.). Engineering & Integration • Work closely with data engineering teams to acquire, transform, and pipeline data from core banking systems, CRMs, claims systems, and real-time feeds. • Design architecture for data lakes, feature stores, and vector databases supporting AI and GenAI use cases