Atlas AI
Distributed AI infrastructure platform for transformer systems, inference optimization, scaling analysis, observability, and performance engineering.
PythonTransformersDistributed SystemsPerformance
View Code →Distributed ML · Optimization · Systems
Omprakash Sahani
ML Systems Engineer
I build distributed training systems, autograd engines, and performance-focused ML infrastructure from first principles—analyzing how memory, communication, and compute constraints shape real-world performance.
Selected Work
Systems projects focused on performance, scalability, and real-world behavior
Distributed Training Profiler
Systems-oriented profiler for analyzing communication overhead, memory bottlenecks, scaling efficiency, and distributed training behavior in large-scale ML workloads.
PythonDistributed SystemsPerformanceMemory
View Code →Benchmark Guardian
Developer infrastructure platform for automated benchmark regression detection, performance analysis, and GitHub pull request feedback in ML systems and backend workflows.
PythonFastAPIGitHub AppsPerformance
View Code →Distributed Training
Distributed training simulator analyzing scaling efficiency, communication overhead, and system-level bottlenecks across data-parallel workloads.
PythonDistributed SystemsAll-ReducePerformance
View Code →Autograd Engine
Reverse-mode autodiff engine with dynamic computation graphs and topological backpropagation. Verified gradient correctness and analyzed trade-offs between memory usage, execution efficiency, and graph flexibility.
PythonAutogradBackpropagationPerformance
View Code →ML Reproducibility Auditor
CLI-based ML reproducibility auditor that evaluates repositories for engineering quality, system design patterns, and reproducibility signals using GitHub API analysis.
PythonCLIReproducibilityML Systems
View Code →ML Experiment Tracker
CLI-based experiment tracking system for reproducible ML workflows, enabling structured run logging, metric comparison, and evaluation across experiments.
PythonCLIReproducibilityML Systems
View Code →Journey
From foundations to ML systems engineering
2017 — 2020
Diploma in Computer Engineering
Built foundational knowledge in programming, data structures, and core system concepts, forming the base for further study in computer science and engineering.
2020 — 2023
B.Tech in Computer Science and Engineering
Built strong foundations in computer science, including data structures, operating systems, distributed systems, and machine learning, shaping a systems-oriented approach to problem solving.
2023 — Present
ML Systems Engineering (Independent)
Building distributed training systems, autograd engines, and performance benchmarking tools from first principles.
Next
ML Systems Engineering & Research
Moving toward building and studying large-scale machine learning systems, combining distributed systems, optimization, and performance engineering to understand how models behave under real-world constraints.