Hey, I'm Shashank Reddy Sama — a Machine Learning Engineer at JPMorgan Chase, building fraud detection, LLM systems, and time-series forecasting at scale. I live in the messy middle between research and production.
ML Engineer · 900 followers
The part where a beautiful model meets a real dataset, a latency budget, and a regulatory requirement — and has to still work. That's the problem I want to solve for the rest of my career.
I'm a Machine Learning Engineer at JPMorgan Chase, where I build the systems that help one of the world's largest banks detect fraud, forecast financial outcomes, and make smarter decisions at scale.
Before JPMC, I was at Accenture building LLM-powered chatbots and NLP pipelines. Before that, I earned my MS in Mechatronics & Robotics at NYU — where I got obsessed with teaching machines to see and make decisions in the real world.
I'm always down to talk about deploying ML responsibly in high-stakes environments, the jump from robotics research to production ML, and why MLOps is the most underrated skill in the industry.
Production ML isn't one skill — it's the unglamorous combination of modeling, infrastructure, data engineering, and the patience to debug a 4 a.m. inference timeout.
Languages, frameworks, infra, and data tools I reach for daily.
From enterprise chatbots to fraud detection running on millions of daily transactions — every role taught me something about the gap between a model that works and one that scales.
A mix of NYU capstones, undergrad research, and personal explorations across computer vision, NLP, and robotics. The unifying thread: making machines perceive and decide in messy, real environments.
Capstone at NYU. Real-time waste classification using YOLO + OpenCV, driving 6 xArm robotic arms positioned along a conveyor belt to sort recyclables vs. non-recyclables.
Autonomous robot that navigates an unknown maze using Visual SLAM for mapping, sensor fusion for perception, and A* path planning to find the shortest route on subsequent runs.
Simulation of a swarm of warehouse robots in Gazebo + ROS2. Designed coordination algorithms for task distribution, inventory management, and order fulfillment under realistic spatial constraints.
CNN-based pipeline for tumor detection on MRI scans. Benchmarked ResNet, VGG, and custom architectures — VGG won. Added a segmentation step to highlight tumor regions for diagnosis support.
Real-time face mask detection on live video feeds using CNN + YOLO, augmented with ANN for decision-making. Built during the pandemic for public-health screening checkpoints.
A Segway-style two-wheeled robot built on the inverted pendulum principle. PID-controlled balance, machine vision for obstacle avoidance, and a custom web app for remote control over a Raspberry Pi.
WiFi-trilateration-based positioning for indoor environments where GPS fails. Real-time signal-strength processing for navigation in malls, hospitals, and office buildings.
High-precision trajectory tracking for a SCARA robotic arm. Modeled kinematics + dynamics in MATLAB, simulated in Simulink, and tuned controllers for assembly-line accuracy.
An autonomous wheeled robot for warehouse logistics. Follows RFID-tagged paths, detects obstacles, and stops safely. Built on Arduino + IoT for distributed coordination.
Efficient iris boundary detection in MATLAB — isolating iris from surrounding ocular structures for biometric ID systems. Optimized for accuracy under tight latency budgets.
Research into reducing the memory footprint and latency of dense neural networks via quantization, while preserving accuracy — an essential trick for edge deployment.
Remote-controlled surveillance robot with live video streaming via a custom web app. Raspberry Pi core, machine vision for movement detection, and IoT for over-the-internet control.
Capstone: Automated garbage sorting (YOLO + 6 xArm robotic arms). Coursework spanned ROS2, Visual SLAM, deep learning, and computer vision.
Foundations in Python, TensorFlow, signal processing, and embedded systems. Built brain tumor detection, AGV, and IPS systems here.
Mathematics, physics, and chemistry. The grind that taught me that hard problems usually just need a clean first principles attack.
Where the curiosity for how things work first kicked in. School competitions, science fairs, and a lot of disassembled household electronics.
Comfortable working across multilingual teams.
If you're hiring, collaborating on a side project, or just want to swap notes on fraud detection, robotics, or computer vision — my DMs are open. Always down to talk shop.