Jayesh Sharma
AI Engineer at Composio
Bengaluru, Karnataka, India
Summary
🤩
Rockstar🎓
Top SchoolJayesh Sharma is an AI engineer with 7 years of experience building and scaling cloud-native ML systems, currently developing agent-based solutions at Composio after contributing to ZenML’s MLflow deployment workflows. Comfortable across MLOps, platform engineering and cloud infra, he has shipped production-focused features like service search/delete, configurable timeouts and cleaner deployment lifecycles in a widely used open-source project (ZenML). An IIT Bhubaneswar graduate and long-time cloud enthusiast, he moves quickly between emerging frameworks and languages and has hands-on experience with Kubernetes, Prometheus exporters, and CI/CD proofs-of-concept. He balances technical curiosity with public speaking and writing, and brings a practical mindset from internships and community open-source work to solve real-world problems.
7 years of coding experience
5 years of employment as a software developer
ICSE, ICSE at Holy Cross School, AgartalaBTech Electronics and Communications Engineering, BTech Electronics and Communications Engineering at Indian Institute of Technology Bhubaneswar (IIT Bhubaneswar)
Marwari, Bengali, Hindi, English
Github Skills (15)
mlflow10
mlops10
python10
terraform9
dockers9
kubernetes-pods9
machine-learning9
docker9
terraformer9
kubernetes9
workflow-engine8
devtools8
pipeline8
developer-tools8
devops8
Programming languages (13)
Github contributions (5)
zenml-io/zenml
Jan 2022 - Jan 2023
ZenML 🙏: The bridge between ML and Ops. https://zenml.io.
Role in this project:
MLOps Engineer
MLOps EngineerContributions:293 reviews, 120 commits, 170 PRs in 1 year
Contributions summary:Jayesh's commits primarily focus on integrating and configuring MLflow model deployment within the ZenML framework. They refactored the MLflow model deployer step, incorporating deployment decisions and parameters. The commits also addressed service cleanup and incorporated features such as user-configurable timeouts. The user also implemented search and delete functions for services, enhancing the overall deployment workflow.
devops-toolspythonproduction-readytensorflowproduction
wjayesh/mahilo
Sep 2024 - Apr 2025
mahilo: Multi-Agent Human-in-the-Loop Framework is a flexible framework for creating multi-agent systems that can each interact with humans while sharing relevant context internally.
Contributions:6 releases, 1 review, 39 PRs in 6 months
agentsllmmulti-agent
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