Role Summary:
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.
Key Responsibilities:
• 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.
•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.
•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.
• 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.
Requirement:
• Core ML: 8+ 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.
•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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Job Family Group:
Finance------------------------------------------------------
Job Family:
Regulatory Reporting------------------------------------------------------
Time Type:
Full time------------------------------------------------------
Most Relevant Skills
Business Acumen, Change Management, Communication, Data Analysis, Financial Acumen, Internal Controls, Issue Management, Problem Solving, Regulatory Reporting.------------------------------------------------------
Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter.------------------------------------------------------
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Citi Compensation & Benefits Highlights
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Healthcare Strength — Benefits coverage is positioned as comprehensive, including health, dental, and vision insurance plus on-site clinics, prescription drug support, and disability coverage. Family-building support such as fertility assistance is described as a notable differentiator within the overall package.
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Retirement Support — Retirement benefits are framed as strong, highlighted by a 401(k) with matching and additional plan options like a Roth 401(k). Financial support is reinforced through discounts and broader financial guidance resources tied to the benefits ecosystem.
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Wellbeing & Lifestyle Benefits — Wellbeing support extends beyond insurance through programs like an Employee Assistance Program, counseling/legal resources, and gym or wellness reimbursement. These offerings increase the perceived total rewards value even when cash compensation sentiment varies by role.
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