Senior AI & Machine Learning Product Lead — FinTech AI agents

Lead the development of an AI-agent platform that autonomously analyzes financial transactions, customer activity, cash flow, and risk signals to support FinTech operations. The system will use LLM-based agents, ML models, RAG, and real-time financial data to investigate anomalies, assess risk, generate financial insights, and recommend actions. Key Responsibilities: Define the AI-agent architecture, product roadmap, agent workflows, and evaluation strategy. Design specialized agents for fraud investigation, transaction analysis, risk assessment, cash-flow analysis, and financial reporting. Combine deterministic financial rules with ML predictions and LLM reasoning rather than relying solely on LLM outputs. Build agent orchestration using LangGraph/LangChain, tool calling, structured outputs, memory, and RAG. Develop ML pipelines for anomaly detection, behavioral scoring, transaction classification, and risk prediction. Implement human-in-the-loop approvals, confidence scoring, audit trails, and agent observability. Establish evaluation frameworks for agent accuracy, hallucination detection, tool-use reliability, and financial decision quality. Work with engineering teams to productionize agents using Python, FastAPI, PostgreSQL, AWS, Docker, and MLflow. Core Technologies: Python, PyTorch, Scikit-learn, XGBoost/LightGBM, LangGraph, LangGraph, LLMs, RAG, vector databases, PostgreSQL, FastAPI, AWS, Docker, MLflow, REST APIs, Databricks, and event-driven architectures. Core ML Libraries: - Deep Learning: TensorFlow, PyTorch, Lightning - Classical ML: Scikit-learn, XGBoost, LightGBM - NLP/LLMs: Hugging Face Transformers, spaCy - Hyperparameter Tuning: Optuna, Ray Tune Infrastructure & Tools: - Cloud: AWS/GCP/Azure (S3, BigQuery, Sagemaker) - MLOps: MLflow, Kubeflow, Prefect - Data: SQL, Pandas, PySpark, Dask - Deployment: Docker, Kubernetes, FastAPI Expected Outcome: A production-ready multi-agent FinTech intelligence system where AI agents investigate financial events, combine ML predictions with financial rules, retrieve supporting data, explain their reasoning, and route high-risk decisions to human reviewers. Skills Artificial Intelligence (AI) Machine Learning AI Agent Development RAG PyTorch Scikit-learn Databricks MLOps