Akshay Malik
Engineering Manager at Anyscale
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolAkshay Malik is an Engineering Manager based in the San Francisco Bay Area with three years of professional experience and a strong foundation in computer engineering from UIUC and an MBA from UC Berkeley Haas. At Anyscale he leads teams building production-grade ML serving and deployment workflows, drawing on hands-on MLOps contributions to the high-profile Ray project—particularly around FastAPI/Transformers model serving, autoscaling, and stable diffusion templates. He blends technical leadership with practical product sense, helping bridge developer experience and operational reliability for ML workloads. Early research and internship roles at Illinois and Qualcomm hint at a long-standing interest in systems and applied research. Colleagues would describe him as a manager who still contributes code and documentation, making him effective at translating strategy into deployable, well-documented systems.
3 years of coding experience
1 year of employment as a software developer
Mount St. Mary's School - Delhi, IndiaMaster of Business Administration - MBA, Master of Business Administration - MBA at University of California, Berkeley, Haas School of BusinessUniversity of Illinois Urbana-Champaign
Github Skills (13)
machine-learning10
lm10
ray10
fastapi10
python10
llm10
autoscaling9
dockers5
docker5
kubernetes4
kubernetes-pods4
tensorflow3
pytorch3
Programming languages (3)
Github contributions (5)
ray-project/ray
Mar 2023 - Mar 2026
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Role in this project:
MLOps Engineer
MLOps EngineerContributions:153 reviews, 23 PRs, 110 pushes in 3 years
Contributions summary:Akshay primarily contributed to the deployment and serving aspects of the Ray project. Their work included implementing and documenting serving applications, especially focusing on serving ML models using frameworks like FastAPI and Transformers. Key contributions involved setting up autoscaling, specifying resource requirements, and updating configurations for model serving. The user also focused on improvements and additions to existing templates for stable diffusion serving and LLM quickstart guides.
aimachine-learningraydistributedparallel
anyscale/templates
Jul 2023 - Jan 2026
Contributions:133 reviews, 124 PRs, 455 pushes in 2 years 6 months
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.