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Lead AI/ML Engineer – Regulatory Reporting

India, Mumbai · Job Posted January 31, 2026
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Job Description

We are establishing a specialized AI/ML team in Mumbai to modernize our Regulatory Reporting function. We are looking for a hands-on technical leader to build and deploy two classes of solutions: (1) Anomaly Detection Models to catch data quality issues before they reach the regulators, and (2) GenAI Workflows (LLMs/Agents) to automate manual reconciliation and document reviews. Crucially, you will solve the 'Validation Bottleneck.' You will design 'Human-in-the-Loop' (HITL) workflows that make it easy for business users to validate model outputs against legal loan documents, bridging the gap between 'Black Box' AI and auditable regulatory standards.

Job Responsibility

  • Build unsupervised and semi-supervised ML models (e.g., Isolation Forests, Autoencoders) to scan millions of transactional records for outliers
  • Go beyond simple 'threshold checks' to detect complex patterns
  • Reduce false positives to ensure the Reporting Team trusts the model alerts
  • Design RAG (Retrieval-Augmented Generation) pipelines to 'chat' with unstructured data (Credit Agreements, Loan Docs) and extract key regulatory attributes (Maturity Dates, Collateral Clauses)
  • Build 'Agentic' workflows where GenAI proactively suggests mapping logic or identifies the root cause of a break, requiring only a 'thumbs up/down' from the human SME
  • Build 'Explainability' (XAI) into every model
  • Create Validation Interfaces: Build simple UIs (using Streamlit or React) where business users can see the Model's Prediction side-by-side with the Source Document to rapidly approve/reject the finding
  • Work with Model Risk Management (MRM) to establish a 'fast-track' validation framework for non-deterministic GenAI models
  • Hire and mentor a squad of 4-5 junior data scientists/engineers in Mumbai
  • Act as the 'AI Evangelist' to the Operations/Finance teams, demonstrating how AI assists them rather than replacing them

Requirements

  • 8+ years in Data Science/Engineering
  • Deep experience with Scikit-learn, TensorFlow, or PyTorch
  • Hands-on experience with LLM orchestration frameworks (LangChain, LlamaIndex) and Vector Databases (Pinecone, Milvus, or pgvector)
  • Experience building tools like Streamlit or Gradio for rapid prototyping of human-review interfaces
  • Experience in Financial Services (specifically Fraud Detection, AML, or Risk Modeling)
  • Able to communicate and explain 'Hallucination Risk' to a non-technical Chief Risk Officer
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