**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. ------------------------------------------------------ **Job Family Group:** Technology ------------------------------------------------------ **Job Family:** Applications Development ------------------------------------------------------ **Time Type:** Full time ------------------------------------------------------ **Most Relevant Skills** Please see the requirements listed above. ------------------------------------------------------ **Other Relevant Skills** For complementary skills, please see above and/or contact the recruiter. ------------------------------------------------------ _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._ _If you are a person with a disability and need a reasonable accommodation to use our search tools and/or apply for a career opportunity review_ _Accessibility at Citi (https://www.citigroup.com/citi/accessibility/application-accessibility.htm)_ _._ _View Citi's_ _EEO Policy Statement (https://www.citigroup.com/global/eeo-aa-policy)_ _and the_ _Know Your Rights (https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf)_ _poster._ Citi is an equal chance and affirmative action employer. Minority/Female/Veteran/Individuals with Disabilities/Sexual Orientation/Gender Identity.

📌 Senior Lead Python AI/ML Engineer (Chennai)
🏢 Citigroup
📍 Chennai