Senior Lead Python AI/ML Engineer

Citigroup Inc.; Chennai; On-site; INR 3,500,000 - 5,500,000; Full time

Job summary

Citi is seeking a Senior Lead Python AI/ML Engineer in Chennai to architect and deployGenAI-driven fraud mitigation and risk scoring solutions. The role emphasizes building scalable Python microservices, integrating LLMs, vector databases, and modern MLOps practices.

You will mentor engineers and shape the GenAI technical roadmap in a hybrid work setup. The candidate brings 10–12 years of software/ML experience, strong TensorFlow/PyTorch, and a track record of production GenAI deployments in

Qualifications

• Bachelor’s or higher degree in a quantitative field preferred.
• 10–12 years of hands-on software and ML engineering experience.
• Proven track record in production GenAI applications and risk-focused ML.
• Strong Python, PyTorch/TF, and ML pipeline development experience.

Responsibilities

• Architect, build, and deploy GenAI systems (RAG, agents, vector search) with low latency.
• Lead production ML algorithms for fraud risk scoring and loss prevention.
• Design scalable microservices and APIs in Python (FastAPI/Flask).
• Mentor engineers; drive technical roadmap and PoCs for GenAI adoption.

Job description

## Senior Lead Python AI/ML EngineerApply: Hybrid: DLF CYBERCITY 12B: Pune Maharashtra India: Full time: Posted Today: 26996796**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.