Senior Lead Python AI/ML Engineer

Citi; Chennai District; Hybrid; INR 3,000,000 - 6,000,000; Full time

Job summary

Citi in Chennai is seeking a Senior Lead Python AI/ML Engineer with Generative AI capabilities to design, deploy, and scale high‑impact fraud and risk solutions for US Retail Banking.

You will lead GenAI architecture, ML models, and MLOps in a hybrid setup, mentoring teams and delivering real‑time, low‑latency services across global nodes.

Qualifications

• Bachelor’s or Master’s in CS/Data Science/AI or related field.
• 10–12 years of software and production AI/ML experience.
• Advanced Python, ML frameworks, and GenAI experience.
• Experience with LLMs, vector databases, and MLOps practices.

Responsibilities

• Architect, build, and deploy production GenAI systems with RAG and multi‑modal workflows.
• Lead scalable Python microservices and data pipelines for high throughput.
• Collaborate with Risk/Compliance teams on fraud and loss prevention solutions.
• Mentor data scientists and engineers; drive technical roadmap and PoCs.

Skills

Python (Advanced)

SQL

Bash

PyTorch

TensorFlow

Scikit-learn

XGBoost

LightGBM

LangChain

LlamaIndex

GenAI Concepts

Education

Bachelor’s or Master’s in CS/Data Science/AI

Tools

Pinecone

Milvus

Qdrant

Weaviate

FAISS

pgvector

Docker

Kubernetes

Kafka

PySpark

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

Category

Technologies & Tools

Programming Languages

Python (Advanced), SQL, Bash

Generative AI & LLMs

Anthropic Claude, Google Gemini, OpenAI GPT, Open‑source LLMs (Llama, Mistral), LangChain, LlamaIndex, DSPy

Classical & Deep ML

PyTorch, TensorFlow, Scikit‑Learn, XGBoost, LightGBM, Hugging Face Transformers

Vector DBs & Search

Pinecone, Milvus, Qdrant, Weaviate, FAISS, pgvector, Elasticsearch

Backend & APIs

FastAPI, Flask, AsyncIO, gRPC, REST

Data & Streaming

Apache Kafka, Apache Spark / PySpark, Pandas, Polars, DuckDB

MLOps & DevOps

MLflow, Kubeflow, Docker, Kubernetes, GitHub Actions, Terraform, Prometheus/Grafana

Domain Tools & Standards

Model Risk Management (SR 11‑7), Explainable AI (SHAP, LIME), PII Masking

Key Performance Indicators (KPIs)

• Model Performance & Accuracy: Precision/Recall trade‑offs in fraud detection, false positive reduction, and sub‑100ms real‑time inference latency.
• Engineering Excellence: Code quality, test coverage (>85%), modular design, and robust error handling.
• Business Impact: Measurable reduction in fraud loss basis points and acceleration of investigator workflow efficiency through GenAI summarization/triage.
• Mentorship & Global Delivery: On‑time milestone delivery, technical capability elevation across local and global engineering pods.

Job Family Group: Technology

Job Family: Applications Development

Time Type: Full time

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