Senior Lead Python AI/ML Engineer

Citi; New York (NY); Hybrid; USD 32,000 - 63,000; Full time

Job summary

Citi is seeking a Senior Lead Python AI/ML Engineer with Generative AI capabilities for its Chennai center, operating in a hybrid model. With 10–12 years of hands‑on software and ML experience, you will drive AI architectures and real‑time risk solutions across Fraud Mitigation, Risk Scoring, and Loss Prevention for US Retail Banking products.

You will design scalable systems, integrate vector DBs, and lead MLOps practices while mentoring engineers and collaborating with global teams.

Qualifications

• 10–12 years in software and AI/ML production systems.
• Advanced Python expertise (asyncio, multiprocessing).
• Hands-on GenAI with LLMs and prompt engineering.
• Experience with cloud platforms and MLOps toolchains.
• Strong knowledge of fraud risk domains and real‑time inference.

Responsibilities

• Architect and deploy production GenAI systems and RAG pipelines.
• Lead scalable microservices and high‑throughput APIs in Python.
• Integrate vector databases and streaming platforms for real‑time analytics.
• Mentor Chennai engineers in GenAI patterns and production readiness.
• Collaborate with Risk, Compliance, and Loss Prevention teams.

Skills

Python

AI/ML engineering

Generative AI

LLMs

MLOps

Distributed systems

Cloud platforms

Education

Bachelor's or Master's in CS/Math/AI

Tools

Pinecone

Milvus

Qdrant

Chroma

pgvector

Kafka

RabbitMQ

MLflow

Weights & Biases

Docker

Kubernetes

PySpark

Ray

LangChain

Job description

Senior Lead Python AI/ML Engineer

Job Req Id:

26996796

Location(s):

Chennai, Tamil Nadu, India, Pune, Maharashtra, India

Job Type:

Hybrid

Posted:

Oct. 13, 2026

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Job Overview

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‑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.

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Job Family Group:

Technology

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Job Family:

Applications Development

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Time Type:

Full time

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Most Relevant Skills

Please see the requirements listed above.

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Other Relevant Skills

For complementary skills, please see above and/or contact the recruiter.

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