Senior Lead Python AI/ML Engineer

Citigroup

India

Hybrid

INR 4,000,000 - 7,000,000

Full time

3 days ago

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Job summary

Citigroup is seeking an experienced Senior Lead Python AI/ML Engineer at our Chennai center in a hybrid work model. You will design, build, and deploy scalable GenAI-based AI/ML solutions focused on fraud mitigation, risk scoring, and loss prevention for US Retail Banking products.

Strong leadership and hands-on expertise across Python, PyTorch, LangChain, and MLOps are required. Role emphasizes architecture of GenAI pipelines, RAG and deployment of low-latency systems, with collaboration across

Qualifications

10–12 years of professional software and ML engineering experience.

Responsibilities

Architect, build, and deploy production-grade GenAI systems (RAG, autonomous agents, semantic search).
Integrate LLMs (Gemini, Claude, open-source models) with latency and cost optimizations.
Lead ML algorithms for real-time risk assessment and fraud mitigation.
Design scalable Python microservices and APIs (FastAPI/Flask).
Implement MLOps/LLMOps pipelines with observability and model drift monitoring.

Skills

Python

GenAI

ML engineering

Distributed systems

Cloud platforms

MLOps

Education

Bachelor's/Master's in CS/AI

Tools

PyTorch

TensorFlow

LangChain

LlamaIndex

Kubernetes

Docker

Job description

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

Key Responsibilities

AI/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).

Technical Leadership & Mentorship

Mentor senior/mid-level data scientists and software engineers across the Chennai engineering hub in modern GenAI patterns, clean code principles, and production readiness.

Drive technical roadmap, evaluate emerging AI frameworks, conduct proof-of-concepts (PoCs), and present architectural proposals to global engineering leadership.

Required Qualifications & Experience

Education: Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Applied Mathematics, or a related quantitative field.

Total Experience: 10 to 12 years of professional experience in software engineering and production AI/ML system development.

Core Python Expertise: Advanced proficiency in Python 3.x (multiprocessing/asyncio, design patterns, profiling, packaging, and clean code paradigms).

Core ML Frameworks: Deep practical experience with PyTorch, TensorFlow, Scikit-learn, XGBoost, and LightGBM.

Generative AI Proficiency: Demonstrated success building production GenAI applications (LangChain, LlamaIndex, CrewAI, AutoGen).

Hands-on experience with embedding models, dense retrieval, rerankers, and vector indexing strategies.

Distributed Systems & Cloud: Strong hands-on experience with cloud platforms (AWS, GCP, or Azure), containerization (Docker, Kubernetes), and distributed data processing (PySpark, Ray).

MLOps / LLMOps: Proven track record in orchestrating end-to-end ML lifecycles from data validation to real-time inference monitoring.

Preferred & Good-to-Have Skills

Google Gemini & Anthropic Claude: Hands-on experience with API integration, function calling/tools, context caching, and prompt optimization specifically for Google Gemini (Pro/Flash) and Anthropic Claude (Opus/Sonnet/Haiku).

Domain Knowledge (Fraud & Loss Prevention): Experience designing fraud rule engines, anomaly detection platforms, or predictive risk models.

Familiarity with transaction monitoring, biometric behavioral analytics, and fraud consortium datasets.

Technical Skills Matrix