Senior Lead Python AI/ML Engineer
citi
Job Description
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