Full-Time

DE&a – Aiml

Data Science, Data Science','Other

Posted on 10/31/2025

Zensar

Zensar

No salary listed

India

In Person

Category
Data & Analytics (2)
,
Required Skills
Scikit-learn
Pinecone
Data Lake
Microsoft Azure
Tensorflow
Pytorch
BigQuery
Xgboost
OpenAI
MLflow
AWS
LangChain
Google Cloud Platform
Requirements
  • 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
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 regulatory compliance
  • Engineering & Integration: Work 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

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