Senior Lead Python AI/ML Engineer

Citigroup Inc.; Chennai District; Hybrid; INR 4,000,000 - 7,000,000; Full time

Benefits offered by this job

Hybrid work model

Career growth & mentorship

Job summary

Citi is seeking a Senior Lead Python AI/ML Engineer with Generative AI capabilities to design and deploy next‑gen AI/ML solutions from our Chennai center in a Hybrid model. You will drive GenAI architectures, manage latency‑critical Python services, and mentor engineers across the Chennai hub.

Responsibilities include building RAG pipelines, integrating LLMs, and ensuring regulatory compliance in banking AI applications, with a focus on fraud mitigation and risk scoring.

Qualifications

• 10–12 years of hands-on software and ML engineering experience.
• Experience designing and deploying GenAI systems in production.
• Hybrid work model in Chennai.
• Strong leadership and mentorship capabilities.

Responsibilities

• Architect, build, and deploy GenAI systems (RAG, agents, multi-modal workflows).
• Lead low-latency Python microservices and scalable APIs.
• Collaborate with Risk, Compliance, and Loss Prevention teams.
• Mentor data scientists and engineers across Chennai hub.

Skills

Python

GenAI

MLOps

Distributed Systems

Leadership

Education

Bachelor's/Master's in CS/AI

Tools

FastAPI

Docker

Kubernetes

PySpark

Kafka

Weaviate

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

2. 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.

3. 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.

4. 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.

5. 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

6. Key Performance Indicators (KPIs)

• Model Performance & Accuracy: Precision/Recall trade‑offs in fraud detection, false positive reduction, and sub-100 ms 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

Citi is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law.

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