Senior Lead Python AI/ML Engineer

Role Overview

We are seeking an experienced and visionarySenior 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 ourChennai center (operating in aHybrid work model). With10 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 toFraud 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, includingRetrieval-Augmented Generation (RAG), autonomous agents, semantic search pipelines, and multi-modal AI workflows.
• Integrate state-of-the-art Large Language Models (LLMs)—includingGoogle 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 inPython (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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