Lead who Drives Data Science Solutions

Nilay is an experienced technologist with over 20 years of experience architecting and engineering solutions focused on AI, machine learning, cloud, security, microservices, and high volume transactional systems. His career reflects a passion for public cloud, DevOps, data engineering, Kubernetes, and applied mathematics.

Crafts Scalable Data Pipelines

Realtime data pipelines and stream analytics solutions to enable low-latency analytics, He has production experience with stream processing platforms like Apache Kafka, Apache Flink, and Azure Stream Analytics to rapidly ingest and analyze high-velocity data feeds.

Recent Focus

Nilay has recently focused on developing and deploying AI and ML solutions on public cloud platforms. He has hands-on experience with MLOps and DataOps to streamline machine learning pipelines and processes. His recent work has centered on leveraging Kubernetes, Docker, TensorFlow, Kubeflow, and other scalable compute services to productionize models and ensure reliable, efficient model serving.

Broad Technology Experience

He has production experience building solutions on a broad range of platforms from Kubernetes, TensorFlow, and Databricks to Python, Rust, GoLang, and Azure data stores. His experience positions him well to rapidly deliver technology solutions leveraging his expertise.

Domain-Driven Expertise: Navigating the Intersection of Trading, Finance, Energy and Data Science, Data Engineering, and Cloud Computing

Energy Trading

Nilay is an accomplished technology leader with deep expertise in machine learning, quantitative analysis, and building advanced platforms for trading organizations. As a Tech Lead at Leading Energy Producer, he currently leads development of energy trading solutions for real-time market data analysis that feed into trading desks and algorithms. Major initiatives include leveraging Confluent Kafka and ML models for power price forecasting and designing robust databricks pipelines for mobilizing models.

AI & Language Models with Productivise Research

Previously, Nilay served as a DevOps Lead and Architect at leading digital safty provider where he built classified analytical algorithms and content moderation solutions applying computer vision and NLP techniques. He led modeling of unsafe digital content and implemented microservices in TensorFlow for scalable deployment. Nilay has hands-on experience developing and operationalizing AI, ML & Quant models from prototype to production.

Payments, E-Commerce & Compliance

In the finance sector, Nilay has worked extensively in payments, tax, fraud detection, and regulatory systems. At top e-commerce retailer, he headed PCI-DSS and GDPR strategy for the payments domain and delivered architectures to enable security and compliance. During his tenure at Lloyds Bank, he secure multi-tenant Kubernetes configuration allowing of business groups to leverage shared cloud infrastructure.

Kubernetes & Core Platform Engineering

Nilay has also worked with innovative startups, assisting with cloud adoption, CI/CD automation, and transitioning monoliths. For example, at Microsoft Consultancy, he provided Kubernetes and Azure consulting, helping define architectures for microservices and establishing DevOps pipelines. Additionally, he has led clients such as Shell, National Grid, and M&S on their cloud journey and digital transformation initiatives.