Senior Lead Python AI/ML Engineer
Citi · Pune, India · 1d ago
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:
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Job Family:
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Time Type:
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Most Relevant Skills
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Other Relevant Skills
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