Job
Description
Experience: 15+ years overall | Minimum 10 full-cycle AI/ML project implementations , including GenAI experience Role Summary: We are seeking a AI Architect to lead strategic AI transformation initiatives. 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 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 regulatory compliance 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. Enable seamless integration of AI capabilities into enterprise workflows, customer platforms, and decision engines via APIs and microservices. Required Skills & Experience: 15+ years of experience in AI/ML, data engineering, and cloud architecture. Minimum of 10 end-to-end AI/ML project implementations from use case discovery through to productionization. Proven expertise in: (Any One) AI/ML frameworks : scikit-learn, XGBoost, TensorFlow, PyTorch GenAI/LLM platforms : OpenAI, Cohere, Mistral, LangChain, Hugging Face, vector DBs (Pinecone, FAISS, Chroma) Cloud platforms : AWS, Azure, GCP - including AI/ML & GenAI native services MLOps/LLMOps tools : MLflow, Kubeflow, SageMaker Pipelines, Vertex AI Pipelines Strong experience with data security, governance, model risk management , and AI compliance frameworks relevant to BFSI. Ability to lead large cross-functional teams and engage both technical teams and senior stakeholders. Experience: 15+ years overall | Minimum 10 full-cycle AI/ML project implementations , including GenAI experience Role Summary: We are seeking a AI Architect to lead strategic AI transformation initiatives. 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 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 regulatory compliance 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. Enable seamless integration of AI capabilities into enterprise workflows, customer platforms, and decision engines via APIs and microservices. Required Skills & Experience: 15+ years of experience in AI/ML, data engineering, and cloud architecture. Minimum of 10 end-to-end AI/ML project implementations from use case discovery through to productionization. Proven expertise in: (Any One) AI/ML frameworks : scikit-learn, XGBoost, TensorFlow, PyTorch GenAI/LLM platforms : OpenAI, Cohere, Mistral, LangChain, Hugging Face, vector DBs (Pinecone, FAISS, Chroma) Cloud platforms : AWS, Azure, GCP - including AI/ML & GenAI native services MLOps/LLMOps tools : MLflow, Kubeflow, SageMaker Pipelines, Vertex AI Pipelines Strong experience with data security, governance, model risk management , and AI compliance frameworks relevant to BFSI. Ability to lead large cross-functional teams and engage both technical teams and senior stakeholders.
