Key Responsibilities: 1. Hands-On Execution (The "What") - Model Development: Build, train, and evaluate ML and GenAI models, taking them from experimentation to production with guidance from senior engineers. - Pipelines: Implement and maintain data and inference pipelines, ensuring they meet reliability and latency targets. - Collaboration: Work closely with senior architects and engineers, contributing to code reviews and learning production best practices. 2. Predictive AI & Scale (The "Fast") - Fraud at Scale: Contribute to our Fraud Risk Management (FRM) system, building features and models for streaming inference using Flink/Spark. - Feature Store: Help build and maintain features that serve both high-velocity transactional data and dense vector embeddings. - Optimization: Profile and optimize model serving to improve throughput on our GPU clusters. 3. Generative & Agentic AI (The "Smart") - AI Agents: Support the development of AI Agents that navigate banking interfaces, execute refunds, and detect social engineering attacks. - LLM Fine-Tuning: Assist in fine-tuning SLMs (Small Language Models) on secured financial data for edge deployment. - Safety Guardrails: Implement guardrails and evaluations that prevent hallucination in critical financial advice. The Ideal Candidate Profile : You are a hands-on builder. • You love to code, you are curious about how things work end-to-end, and you are eager to grow into a scale-veteran engineer. • Essential Experience (3+ Years) - Dual Exposure: Hands-on experience in Classic ML AND/OR Modern GenAI (LLMs/Agents). • Bonus if you have shipped either type of system to production. • Scale Curiosity: Experience working with production systems and a strong desire to understand high-throughput, distributed computing. • Deep Learning Fundamentals: Solid grasp of ML/DL fundamentals and the underlying math. • Familiarity with Transformers or GNNs is a plus. • Collaboration: Comfortable working in cross-functional teams and aligning your work with business goals. Must-Have Skills - • Programming: Strong proficiency in Python and solid software engineering fundamentals (clean code, version control with Git, testing). • Core ML: Hands-on experience building, training, and evaluating ML models using scikit-learn, XGBoost/LightGBM, and PyTorch or TensorFlow. • GenAI / LLMs: Practical experience with LLMs – prompt engineering, RAG, fine-tuning, and frameworks such as LangChain/LangGraph and Hugging Face Transformers. • Data Handling: Strong SQL and data wrangling skills (Pandas/NumPy); experience working with large datasets. • Big Data / Pipelines: Working experience with Spark or Kafka/Flink for building data and inference pipelines. • Deployment: Experience containerizing and deploying models (Docker), exposing them via REST/gRPC APIs, and basic familiarity with Kubernetes. - MLOps: Exposure to experiment tracking and model lifecycle tools (MLflow) and CI/CD for ML. • Cloud: Hands-on with at least one major cloud platform (AWS, GCP, or Azure). • Fundamentals: Solid grasp of statistics, probability, and deep learning concepts (including Transformer architecture). • Communication: Ability to clearly document work and collaborate within cross-functional teams. Technical Stack - ML Ops: Kubernetes, Kubeflow, Ray, MLflow. - Big Data: Spark, Kafka, Flink, Iceberg/Hudi. - GenAI: vLLM, LangGraph, DeepSpeed, Unsloth. - Cloud/Infra: Working knowledge of GPU/TPU usage and cost awareness. The Package Growth: Learn directly from Principal Architects and senior engineers shaping the nation's AI. Resources: Access to state-of-the-art compute infrastructure and proprietary datasets that are unique in the world. Legacy: The opportunity to say, "I helped build the AI that powers the Indian Economy."