This list contains only the countries for which job offers have been published in the selected language (e.g., in the French version, only job offers written in French are displayed, and in the English version, only those in English).
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