OpenOpportunity Sourced Jobs — Mumbai
Full Time · On Site · Entry Level
Role: Data Science & Machine Learning - Other | Department: Data Science & Analytics | Industry: Financial Services | Education: UG: B.Tech / B.E. in Any Specialization PG: M.Tech in Any Specialization, MCA in Computers, MS/M.Sc(Science) in Any Specialization, LLM in Law You will be part of NPCI Market Innovation team, working at the intersection of advanced machine learning, deep learning, graph AI, and Generative AI to build next-generation intelligent systems for India digital payments ecosystem. This role focuses on solving India-scale problems such as fraud detection, mule/AML risk modeling, transaction intelligence, and conversational AI, using both classical ML and cutting-edge AI architectures (LLMs, GNNs, Transformers, Agentic AI systems). You will design end-to-end AI systems from problem formulation, feature engineering, and model development to GPU-accelerated optimization and production deployment, ensuring low latency, scalability, and robustness. The role offers a unique opportunity to work on: Graph-based fraud detection systems Agentic AI & LLM-powered platforms (RAG, MCP, workflows) GPU/CUDA optimized AI pipelines Privacy-preserving and federated AI systems Job Details Job Title: Data Scientist & AI Engineer Division: NPCI Data Analytics & Market Innovation Education: B.Tech / M.Tech / MSc / MCA (PhD preferred) in CS, AI, DS, Mathematics or related field Employment Type: Full-time Location: Hyderabad Role Type: Permanent Key Responsibilities Machine Learning & Advanced Modeling: Develop and deploy ML/DL models (Logistic Regression, RF, XGBoost, NN, CNN, Transformers, GANs). Build models for fraud detection, AML, anomaly detection, transaction intelligence. Work on imbalanced datasets using advanced sampling and cost-sensitive learning. Graph AI & Advanced Systems: Design Graph AI models: GNN, GCN, GAT, temporal graph networks. Apply network analytics for fraud rings, mule detection, behavioral risk signals. Generative AI & Agentic Systems: Build LLM-powered applications (chatbots, complaint intelligence, document analysis). Implement RAG pipelines. Agentic workflows & MCP (Model Context Protocols). Prompt engineering, LLM fine-tuning. Feature Engineering & Data Science: Perform EDA, feature engineering (temporal, behavioral, aggregated features). Work with structured, semi-structured, and unstructured data. Model Optimization & GPU Acceleration: Optimize models for latency, throughput. GPU performance (CUDA-based optimization). Use libraries such as RAPIDS, cuDF, cuML, cuGraph. Evaluation & Experimentation: Design custom loss functions (weighted BCE, cost-sensitive). Apply business-aligned metrics: Precision, Recall, ROC-AUC, PR-AUC. Use robust validation techniques (cross-validation, time-based splits). Deployment & Production Systems: Integrate models into batch and real-time production systems. Design scalable ML pipelines & APIs. Monitor model drift, performance stability, business impact. Collaboration & Research: Work with data engineers, product teams, and business stakeholders. Contribute to research, innovation, and academic collaborations. Stay updated on latest AI advancements (LLMs, Graph AI, Federated Learning). Requirements Required Technical Skills Core ML & Data Science: Strong in supervised & unsupervised learning, statistical modeling (Logistic Regression, DA), tree models (RF, XGBoost, LightGBM), Deep Learning (NN, CNN, Transformers, GANs). Generative AI & LLM Stack: Hands-on experience with LLMs (OpenAI, open-source models), prompt engineering, fine-tuning, RAG pipelines & vector databases, agent frameworks & MCPs. Graph AI: Experience with GNN, GCN, GAT, graph-based fraud detection. Programming & Tools: Strong proficiency in Python (NumPy, Pandas, scikit-learn), SQL (large-scale data processing). Frameworks: PyTorch / TensorFlow, PyTorch Geometric. Key Skills and Experience Required: Strong foundation in mathematics, probability, statistics, data structures & algorithms. Expertise in feature engineering & model evaluation, handling large-scale datasets. Experience with imbalanced datasets & sampling techniques, custom loss functions & business metrics. Knowledge of model deployment & production pipelines, model monitoring & performance tracking. Strong problem-solving ability, communication & stakeholder management. Ability to translate business problems into scalable AI systems. Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying. Note: This is the headline finding for this batch. The job's own "Job Details" block states "Location: Hyderabad" and "Job Title: Data Scientist & AI Engineer" -- yet this listing is filed on Naukri under Mumbai and titled "Associate Data Science," advertised at a junior "1-3 years" band. The body also states "Education... (PhD preferred)" and describes extremely advanced technical scope (GNNs, Transformers, GPU/CUDA optimization with RAPIDS/cuDF/cuML, agentic LLM systems) that reads far closer to a senior research-engineer role than a 1-3 year junior "Associate" position. This is one of three near-identical NPCI postings found in this search (see Jobs 2 and 3 below, both filed under Hyderabad, where the embedded "Location: Hyderabad" line is internally consistent) -- strong evidence the same master job description was fanned out across multiple cities without updating the Mumbai copy's embedded location field.
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