Oct 2023 – Mar 2024 · 5 mos · Bengaluru, Karnataka, India · Remote

• Achieved 20% CPU optimization by implementing asynchronous retries through extraction of Push Notifications to a dedicated job.
• Drove frontend services redesign, consolidating into a single service, resulting in a remarkable 65% reduction in running costs.

Aug 2022 – Oct 2023 · 1 yr 2 mos · Noida, Uttar Pradesh, India · On-site

• Achieved 30% revenue growth by leading the design and development of a cross-platform, multi-tenant, real-time EEG streaming and processing platform. Utilized cutting-edge technologies such as AWS, Electron.js, Node.js, AWS Lambda, Amplify, RxJs, and ReactJs.
• Engineered a cross-platform SDK in Java, Python, and Javascript, enabling seamless interaction with Neuphony EEG hardware via Serial, Dongle, and Bluetooth.
• Developed a compelling proof-of-concept (POC) of a real-time EEG streaming desktop application, showcased at Shark Tank India Season 2 within a remarkable 15-day timeframe.
• Enabled seamless feature rollouts, prioritized bug resolution, and upheld a 98% NPS score through stakeholder collaboration, client engagement, and internal teamwork.
• Achieved seamless software delivery by streamlining the CI/CD pipeline, optimizing release cycles, and ensuring enhanced code quality through rigorous code reviews and comprehensive testing.

Jan 2021 – Jul 2022 · 1 yr 6 mos

• Revamped CRM pipeline with seamless integration of WhatsApp and RCS channels, empowering clients to launch highly customized campaigns, delivering over 1 crore messages in a mere 2 hours using the power of Java, Spring, SpringBoot, Rabbitmq, microservices, MySQL, MongoDB.
• Enhanced and automated credit utilization with dynamic pricing and refund flow, incorporating campaign delivery stats, while introducing a Databricks dashboard for seamless transaction tracking by stakeholders.
• Engineered an automated ML model lifecycle platform using Node.js and TypeScript, driving efficiency and productivity.
• Dramatically boosted efficiency by automating ML model training and inference with Apache Airflow integration, reducing manual effort by 90%.

Jul 2020 – Dec 2020 · 5 mos

• Leveraged customer transaction data to develop cutting-edge CLTV models, including a regression model for precise CLTV predictions and a deep neural network-based classifier with 40%+ accuracy.
• Operationalized CLTV and other models, seamlessly integrating them into Databricks for streamlined execution through Airflow and empowering data-driven decision-making for clients.
• Outperformed state-of-the-art results with groundbreaking recommendation techniques using Knowledge Graph embeddings.
• Constructed a robust knowledge base by integrating user transactions with a comprehensive movies database using Selenium.
• Leveraged Ampligraph to generate powerful knowledge graph embeddings, leading to accurate recommendation predictions.

Jan 2020 – Jun 2020 · 5 mos

Jun 2019 – Jul 2019 · 1 mo

• Hands on experience, developing machine learning models to deploying them to production
• Developed Face and Emotion recognition model.
• Generated their inference scripts.
• Stored the data in mongoDB.
• Deployed the models and the database on AWS EC2 instances.

Jun 2018 – Jul 2018 · 1 mo · New Delhi Area, India

• Undergone 8 weeks of Industrial training in Network Service Division at DESIDOC, Defence Research and Development Organisation (DRDO).
• Worked directly under one of the senior scientists at DRDO Md. Yousuf Ansari.