AI/ML Engineer – Regulatory Reporting

About the job

Key Responsibilities: • ML for Anomaly Detection (The "Watchdog") • Build unsupervised and semi-supervised ML models (e.g., Isolation Forests, Autoencoders) to scan millions of transactional records for outliers. • The Challenge: Go beyond simple "threshold checks." Detect complex patterns (e.g., "This trade structure looks valid in isolation but is anomalous for this specific counterparty type"). • Reduce false positives to ensure the Reporting Team trusts the model alerts. • GenAI & Workflow Automation (The "Builder") • 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. • Solving Model Validation & Governance (The "Diplomat") • This is a critical success factor. You must build "Explainability" (XAI) into every model. You cannot just output a score; you must output why (e.g., "Flagged because this value is 3x higher than the historical average for this product"). • 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. • Act as the "AI Evangelist" to the Operations/Finance teams, demonstrating how AI assists them rather than replacing them. Candidate Profile (The "Mumbai Persona"): • Technical "Must-Haves" • Core ML: 6+ years in Data Science/Engineering. Deep experience with Scikit-learn, TensorFlow, or PyTorch. • GenAI Stack: Hands-on experience with LLM orchestration frameworks (LangChain, LlamaIndex) and Vector Databases (Pinecone, Milvus, or pgvector). • The "Validation" Stack: Experience building tools like Streamlit or Gradio for rapid prototyping of human-review interfaces.

Requirements

• Anomaly Detection
• ML Models
• GenAI Workflows
• Explainability

Qualifications

• 6+ years in Data Science/Engineering

Preferred Technologies

• Anomaly Detection
• ML Models
• GenAI Workflows
• Explainability