Senior Lead Python AI/ML Engineer

Role Overview

We are seeking an experienced and visionary Senior Lead Python AI/ML Engineer with Generative AI Capabilities to lead the design, development, and deployment of next-generation artificial intelligence and machine learning solutions based out of our Chennai center (operating in a Hybrid work model). With 10 to 12 years of hands-on software and machine learning engineering experience, the ideal candidate will drive the convergence of traditional predictive ML models and modern Foundation Model / GenAI architectures.

In this role, you will be instrumental in building high-scale, low-latency AI solutions applied to Fraud Mitigation, Risk Scoring, and Loss Prevention across US Retail Banking products

2. Key ResponsibilitiesAI/ML & Generative AI Architecture

• Architect, build, and deploy production-grade Generative AI systems, including Retrieval-Augmented Generation (RAG), autonomous agents, semantic search pipelines, and multi-modal AI workflows.
• Integrate state-of-the-art Large Language Models (LLMs)—including Google Gemini, Anthropic Claude, and open-source models (Llama, Mistral)—optimizing for latency, cost, and accuracy.
• Build domain-specific prompt engineering frameworks, fine-tuning pipelines (LoRA/QLoRA), model evaluation benchmarks, and guardrails to minimize hallucinations and bias.
• Lead the architecture of predictive ML algorithms (anomaly detection, graph neural networks, supervised/unsupervised classifiers) tailored to real-time risk assessment.

Software Engineering & System Scalability

• Design resilient, low-latency, and horizontally scalable microservices and APIs in Python (FastAPI, Flask, AsyncIO) handling high-throughput transactional traffic.
• Integrate vector databases (e.g., Pinecone, Milvus, Qdrant, Chroma, pgvector) with distributed caching and event streaming platforms (Kafka, RabbitMQ).
• Implement robust MLOps and LLMOps practices (CI/CD pipelines, automated model retraining, observability with MLflow/Weights & Biases, model drift monitoring).

Fraud Analytics & Domain Innovation

• Collaborate closely with US-based and global Risk, Compliance, and Loss Prevention teams to build AI solutions targeting identity theft, account takeover (ATO), synthetic identity fraud, card-not-present (CNP) fraud, and transaction anomaly detection.
• Utilize unstructured data (investigator notes, SAR filings, customer interaction logs) via GenAI to automate fraud triage and investigative summarization.
• Ensure strict adherence to US Banking regulatory standards (FCRA, GLBA, Model Risk Management SR 11-7, Fair Lending, Explainable AI / XAI).