Akshay Malik
Engineering Manager at Anyscale
San Francisco Bay Area United States
Summary
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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 (15)
llm10
machine-learning10
ray10
fastapi10
lm10
deploying10
serve10
python10
autoscaling9
dockers5
docker5
kubernetes4
kubernetes-pods4
pytorch3
tensorflow3
Programming languages (3)
Github contributions (5)
ray-project/ray
Mar 2023 - Apr 2025
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:120 reviews, 20 PRs, 108 pushes in 2 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.
pythonconsistsruntimetensorflowserving
anyscale/templates
Jul 2023 - Sep 2024
Contributions:128 reviews, 112 PRs, 444 pushes in 1 year 2 months
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