# Kaushik Shridhar > ML Engineer | Reinforcement Learning · Computer Vision · NLP | Production AI Systems | PyTorch · CUDA · AWS | MS CS @ IU Bloomington Location: San Francisco, California, United States Profile: https://flows.cv/kaushikshridhar Hi, I'm Kaushik, I build ML systems — from training and optimization to deployment. I care as much about the quality of the underlying model as the system running it reliably at scale. My most recent research has been around reinforcement learning, specifically policy learning and autonomous navigation. I've designed transformer-based policies for agents in multi-target environments and trained on-policy algorithms like PPO and TRPO in custom simulation setups. I also have experience shipping full-stack AI products. At Kahana I'm building a voice-first agentic AI browser with real-time LLM and voice API integration, backed by a scalable cloud backend on AWS. Before that, during my time at IU, I built high-throughput data pipelines on HPC clusters processing tens of thousands of records across ML and research workloads. On the side, I've worked with CUDA writing custom GPU kernels for ML operations. Currently I am actively exploring opportunities for ML roles focused on reinforcement learning, computer vision, or NLP. ## Work Experience ### Software Engineer (AI/ML) @ Kahana Jan 2025 – Present | Chicago, IL • Architected a voice-first agentic AI browser capable of natural language web interaction, covering search, navigation, form automation, and multi-step task execution, built around real-time LLM and voice API integration with Gemini and Deepgram. • Designed the backend infrastructure on AWS using EC2 and DynamoDB, with ElastiCache for low-latency caching to keep real-time LLM inference responsive under load. • Managed infrastructure as code with Terraform and built CI/CD pipelines and observability tooling to support fast iteration cycles while maintaining production reliability. ### Machine Learning Engineer @ Indiana University Bloomington Jan 2025 – Jan 2025 • Designed a transformer-based navigation policy for autonomous agents in multi-target environments where standard RL approaches struggled with long-horizon planning, outperforming baseline methods on path planning tasks. • Built a custom OpenAI Gym-compatible RL environment and trained agents using PPO, TRPO, VPG, and imitation learning, improving obstacle avoidance and task completion by 30%. • Implemented policy gradient methods at the algorithm level, including advantage estimation, reward shaping, and trajectory sampling. ### Graduate Research Assistant - ML & Bioinformatics @ Indiana University Bloomington Jan 2024 – Jan 2024 • Co-developed GRIP, a Boolean convex optimization model for gene regulatory network inference that exceeded state-of-the-art baselines by 20% on large-scale genomic datasets. Co-authored the resulting publication. • Existing pipelines couldn't handle the data volume needed for large-scale motif discovery, so built and deployed high-throughput pipelines on IU's BigRed200 HPC cluster using SLURM, boosting throughput by 25%. • Unified 4 inference frameworks (DirectNet, SCENIC+, Pando, CellOracle) into a single modular Python toolkit, cutting environment setup time by 40% across the team. • Automated biological annotation pipelines via Ensembl and UCSC APIs, processing 30k+ records and improving analysis accuracy by 15%. ### Machine Learning Researcher @ Indiana University - Kelley School of Business Jan 2024 – Jan 2024 • Developed time-series and regression models to forecast hospital patient census and inflow patterns, translating noisy operational data into actionable resource planning signals. • Ran large-scale training and hyperparameter experiments on IU's BigRed200 supercomputer, enabling fast iteration across model architectures and feature configurations. ### Robotics Developer @ Technical Team SIES GST Jan 2020 – Jan 2023 • Designed and built autonomous and interactive robotics systems including multi-sensor navigation vehicles, a voice-interactive robot with sub-300ms response latency, and bots for robot soccer using Arduino, Raspberry Pi, and custom embedded firmware. • Worked across communication protocols (UART, I2C, SPI), real-time control loops, and actuator systems. • Ran workshops for 50+ students and supervised hackathon teams from hardware prototyping through to demo-ready systems. ### Software Developer @ Prishni (Incubated @ NSRCEL, IIM-B) Jan 2021 – Jan 2021 | Bengaluru • Designed and built the backend APIs from scratch, handling 10k+ daily transactions with consistent performance during traffic spikes. • Deployed on AWS using auto-scaling groups and CDN, cutting page load times by 30% and maintaining 99.95% uptime under variable load. • Integrated a PCI DSS-compliant payment system with TLS 1.3 encryption, resulting in a 15% increase in successful transactions and 20% faster payment processing. ## Education ### Master's degree in Computer Science Indiana University Bloomington ### Bachelor of Engineering - BE in Computer Engineering SIES Graduate School Of Technology ## Contact & Social - LinkedIn: https://linkedin.com/in/kaushik-shridhar --- Source: https://flows.cv/kaushikshridhar JSON Resume: https://flows.cv/kaushikshridhar/resume.json Last updated: 2026-04-10