AI Technical Manager

Job Role: AI Technical Manager

Job Location: Bangalore

Work Mode: (Hybrid)

Experience : 10 years

Overview:

• We are looking for an experienced AI/ML Engineer. This is a high-impact, hands-on role where you will design, build, and operationalize enterprise-grade AI and machine learning solutions that transform how we do credit, risk, compliance, fraud detection, and relationship banking.
• You will be embedded in a modern cloud-native AWS Bedrock environment, develop and consume MCP (Model Context Protocol) servers to extend AI agent capabilities, and use GitHub Copilot inside VS Code to write production-quality Python at speed.
• You will own the full lifecycle from raw data to model serving to production observability in a regulated financial services environment.

What You'll Work On

• Intelligent Credit & Loan Underwriting Build ML models for automated credit scoring, risk-tiered loan decisioning, and covenant monitoring.
• Use Amazon Bedrock RAG pipelines to extract and summarize insights from credit memos and borrower financials. Deliver SHAP/LIME explainability layers for regulatory audit readiness.
• MCP Server Development & AI Agent Extensions Design and implement MCP servers in Python that expose internal banking APIs, loan systems, KYC data, and market data as callable tools for Bedrock AI agents.
• Integrate MCP servers into multi-agent orchestration pipelines using Amazon Bedrock Agents and LangGraph.
• Manage MCP server deployments through Azure DevOps CI/CD and ServiceNow change management.
• Conversational AI & Relationship Manager Copilots Deploy Bedrock-powered agent assistants that give Relationship Managers real-time portfolio insights, risk flags, and deal recommendations in plain language.
• Build RAG-based knowledge bases over banking policies, compliance regulations, and product documentation.
• Fraud Detection & Anomaly Intelligence Build real-time and batch ML models for transaction fraud, identity anomalies, and ACH/wire fraud detection.
• Integrate Bedrock LLMs to auto-generate analyst-ready fraud investigation summaries and connect agent tools to alert workflows via MCP.
• Document Intelligence & Extraction Design document processing pipelines using Amazon Textract, Comprehend, and Bedrock for automated extraction from KYC packages, term sheets, and financial statements.
• Expose pipelines as MCP tools for downstream agent consumption.
• Portfolio Risk & Predictive Analytics Build early warning systems for concentration risk, sector exposure, and credit deterioration.
• Develop time-series forecasting models for cash flow, deposit behavior, and interest rate sensitivity all instrumented with Datadog.
• AML & Regulatory Compliance AI Build ML pipelines for AML transaction monitoring and SAR pre-screening.
• Use Bedrock LLMs to draft and summarize regulatory filings and examination responses. Ensure full alignment with SR 11-7, OCC model risk standards, and BSA/AML requirements.

What You'll Do Day-to-Day

• Build end-to-end ML and GenAI pipelines in Python across AWS services (Bedrock, SageMaker, Lambda, Step Functions, ECS, S3, Kinesis)
• Design, implement, and operate MCP servers define tool schemas, implement server handlers, version and deploy into production
• Write clean, tested, type-annotated Python using FastAPI, Pydantic, boto3, LangChain, and LangGraph
• Use GitHub Copilot daily in VS Code for code generation, test scaffolding, docstrings, and PR review acceleration
• Author and maintain CI/CD pipelines in Azure DevOps: YAML pipeline authoring, multi-stage deployments, model validation gates, and artifact management
• Instrument all AI workloads in Datadog: APM, LLM Observability (token cost, latency, hallucination monitoring), custom dashboards, SLOs, and runbook-linked alerts
• Submit all production AI deployments through ServiceNow change management: risk assessment, rollback plan, test evidence, and CAB coordination
• Own the full MLOps lifecycle: MLflow experiment tracking, model versioning, A/B testing, shadow deployment, canary releases, and drift detection
• Maintain model cards, ADRs, MCP server documentation, and data lineage artifacts for model risk governance
• contribute to the team's GitHub Copilot and AI engineering best practices

Must-Haves

• 8+ years of professional experience in ML engineering, AI engineering, or applied data science in production environments
• Expert Python skills: pandas, NumPy, scikit-learn, FastAPI, Pydantic, asyncio, pytest
• Hands-on Amazon Bedrock experience: model invocation, Agents, Knowledge Bases, and Guardrails
• Working knowledge of MCP (Model Context Protocol): building and consuming MCP servers, tool schema design, and agent integration
• Proficiency with Azure DevOps CI/CD: YAML pipelines, environment gates, artifact registries, staged rollouts
• Datadog experience: APM, LLM Observability, dashboards, monitors, and SLO management
• ServiceNow change management: authoring standard/normal change requests, rollback plans, CAB submissions
• GitHub Copilot for day-to-day development within VS Code
• Familiarity with financial services regulatory frameworks: model risk management, explainability, data governance

Nice-to-Haves

• Prior experience in commercial banking, FinTech, or financial services
• AWS Certified Machine Learning Specialty or AWS AI Practitioner
• Experience with LangGraph, CrewAI, or Amazon Bedrock Agents for multi-agent orchestration
• Exposure to SR 11-7, OCC Bulletin 2011-12, or NIST AI RMF