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Best 20 MLOps Engineers You Can Trust

MLOps engineers - Best 20 MLOps Engineers You Can Trust

Machine Learning Operations (MLOps) has emerged as a crucial discipline bridging the gap between developing AI models and deploying them reliably at scale.

Below is a curated and global list the best MLOps engineers, selected for their equal excellence across open-source contributions, technical leadership, influential writing/speaking, industry impact, and competition achievements:

  1. Matei Zaharia
  2. Jeremy Lewi
  3. Abhishek Thakur
  4. Ville Tuulos
  5. Parul Pandey
  6. Luis Ceze
  7. Ketan Umare
  8. Chip Huyen
  9. Hamel Husain
  10. Demetrios Brinkmann
  11. Alejandro Saucedo
  12. Mike Del Balso
  13. Shawn Lewis
  14. Clément Delangue
  15. Josh Tobin
  16. Valliappa Lakshmanan
  17. Aparna Dhinakaran
  18. Dean Pleban
  19. Julien Simon
  20. Luigi Patruno

Now, let’s explore their contributions and impact in detail:

Matei Zaharia

YouTube Video

The future is small models.

Nationality: Romanian

Co-founder & CTO of Databricks and an Associate Professor at UC Berkeley, Matei is the original creator of Apache Spark and a driving force behind MLflow, an open-source platform for the ML lifecycle.

Under his technical leadership, Databricks has spearheaded major MLOps innovations (including Delta Lake and model-serving initiatives). Matei remains an active engineer and researcher, influencing how organizations manage experiments and deploy models at scale.

Jeremy Lewi

Nationality: American

Co-founder and Lead Engineer of Kubeflow, Jeremy was a principal software engineer at Google Cloud who started the Kubeflow project to simplify deploying ML workflows on Kubernetes.

He has been building on Kubernetes since its early days, previously contributing to Google Cloud ML Engine and Dataflow. Jeremy’s leadership on Kubeflow (an open-source MLOps platform) made it possible for developers to train, deploy, and manage models cloud-natively across environments. He remains an active contributor in the Kubeflow community and a prominent voice on cloud ML best practices.

Abhishek Thakur

Nationality: German

The world’s first 4x Kaggle Grandmaster, Abhishek has transitioned from competition fame to real-world ML engineering and education.

He has authored the book “Approaching (Almost) Any Machine Learning Problem” and built popular tutorials guiding ML enthusiasts from modeling to deployment. Abhishek has worked as a data scientist (most recently at Hugging Face) and also created useful open-source libraries (for example, an AutoML library). Known for his practical YT videos, he often covers end-to-end project pipelines and MLOps tips for data scientists.

His unique blend of competition-honed problem solving and industry experience has made him an influential voice making MLOps accessible to the broader ML community.

Ville Tuulos

Ville Tuulos - Best 20 MLOps Engineers You Can Trust

Nationality: Finnish

Co-creator of Metaflow and Co-founder/CEO of Outerbounds, Ville is an expert in ML infrastructure.

At Netflix, he led the machine learning platform team and created Metaflow, an open-source framework to streamline data science pipelines. Now at Outerbounds, he’s extending Metaflow’s vision to help teams rapidly prototype and deploy ML models. Ville’s two decades in ML engineering span academia and industry; he has consistently championed “human-friendly” tooling for serious ML systems. His contributions have influenced how companies handle data pipelines, versioning, and model runtime scaling.

Parul Pandey

Nationality: Indian

A Kaggle Grandmaster (Notebooks) and prominent ML blogger, Parul combines data science expertise with a passion for developer advocacy. She was the first woman in India to earn KaggleG status, and her award-winning analysis notebooks have inspired many in the community.

After working as a Principal Data Scientist at H2O.ai, Parul now focuses on bridging the gap between complex ML tech and end-users through content. She co-authored the O’Reilly book “Machine Learning for High-Risk Applications” and speaks frequently at industry events. Parul is known for her accessible writing on topics like model interpretability, responsible AI, and practical tips for deploying models.

Through her blogs and talks, she has become an influential voice encouraging inclusive and ethical MLOps practices.

Luis Ceze

Nationality: Brazilian

Co-founder & CEO of OctoML and Professor at University of Washington, Luis is a leading figure in model optimization and deployment. He co-authored the Apache TVM project – an open-source deep learning compiler that optimizes models for various hardware backends.

OctoML (a UW spinout he leads) builds on TVM to help businesses seamlessly deploy ML models to production with maximal performance on any device. Luis’s work sits at the intersection of computer architecture and ML: he has shown that co-designing algorithms with compilers and hardware can dramatically speed up model inference. He’s published extensively (Lazowska Endowed Professor at UW) and his research on ML systems has been featured in media.

By driving both research and a startup, Luis pushes the state-of-the-art in efficient, portable ML – crucial for the next generation of MLOps tooling.

Ketan Umare

Ketan Umare - Best 20 MLOps Engineers You Can Trust

Nationality: Indian

Original creator of Flyte and Co-founder/CEO of Union.ai, Ketan built one of the first Kubernetes-native ML orchestration platforms while at Lyft.

Flyte emerged from his experience developing Lyft’s early ride ETA ML models and frustration with glue-code pipelines. Today, Flyte (now a Linux Foundation project) is a widely adopted workflow engine for reliable, reproducible model pipelines. At Union.ai, Ketan continues to actively engineer managed Flyte services, making advanced pipeline orchestration accessible to teams of all sizes.

He regularly shares insights on scaling ML in production and has helped shape the MLOps tooling ecosystem.

Chip Huyen

The hardest part of machine learning is not building the model. It’s everything else.

Nationality: Vietnamese

Co-founder & CEO of Claypot AI, Chip is a prominent MLOps influencer, author, and engineer.

She teaches Machine Learning Systems Design at Stanford and wrote the bestselling book “Designing Machine Learning Systems”. Previously at Snorkel AI and NVIDIA, Chip co-founded Claypot to enable real-time machine learning pipelines. She is known for her vivid blog posts and guides on deploying ML in production (her MLOps Guide and courses are widely read).

Chip’s mix of hands-on coding (she often builds demos) and clear writing/teaching has made her a leading voice helping companies navigate serving and monitoring models in real time.

Hamel Husain

Hamel Husain - Best 20 MLOps Engineers You Can Trust

Nationality: American

A fast.ai core contributor turned MLOps champion, Hamel is known for automating and integrating ML into software development workflows.

As a Staff ML Engineer at GitHub, he helped build GitHub Actions support for ML and co-created nbdev (a tool for literate programming with Jupyter notebooks). Hamel previously worked on Airbnb’s internal ML platform (“Bighead”) and contributed to open-source projects like fastai, fastcore, and fastpages. His work focuses on making ML development more reproducible and developer-friendly.

Hamel’s blogs and talks often cover bridging DevOps and ML (CI/CD for ML, experiment tracking, etc.), drawing from his experience building ML infrastructure at scale.

Demetrios Brinkmann

Nationality: German

Founder of the MLOps Community, Demetrios has built one of the largest global communities of MLOps practitioners. Often donning the title “Chief Happiness Engineer,” he organizes weekly meetups, panels, and the MLOps Coffee Sessions podcast, interviewing leading experts about how to successfully deploy and maintain ML models.

Demetrios “fell into” MLOps after a stint teaching English abroad, and has since become an evangelist for good ML ops practices – including addressing ethical issues in ML deployment. While not a traditional coder, his impact comes from connecting engineers, sharing knowledge, and spotlighting emerging tools and techniques in the field. Through community-building, he’s accelerated the spread of MLOps know-how across continents.

Alejandro Saucedo

Nationality: British

Director of ML Engineering at Seldon Technologies and Chair of the Linux Foundation’s GPU Acceleration Committee, Alejandro is a major contributor to open-source MLOps solutions.

At Seldon, he has led large-scale implementations of ML orchestration and explainability infrastructure for enterprise clients. He’s also Chief Scientist at the Institute for Ethical AI, helping develop standards for bias and privacy in ML. Alejandro has 10+ years of software development experience and has built ML engineering teams from scratch, delivering production ML systems across finance, transport, and other industries.

He is a frequent speaker (e.g., keynotes at KubeCon, AI Summit) and writer on topics like Kubernetes for ML, model explanation techniques, and responsible AI.

Mike Del Balso

Mike Del Balso - Best 20 MLOps Engineers You Can Trust

Nationality: Canadian

Co-founder & CEO of Tecton, Mike pioneered the concept of the Feature Store in MLOps.

At Uber, he was the product lead who helped build the Michelangelo ML platform, overseeing the creation of foundational data and model infrastructure. Now at Tecton, he’s translating that experience into an enterprise feature platform that serves real-time ML data needs. Mike remains hands-on in architecting systems for managing ML features and monitoring data quality in production.

He is also known for co-authoring “Feature Engineering for ML” content and regularly speaks about operationalizing ML data pipelines.

Shawn Lewis

Nationality: American

Co-founder & CTO of Weights & Biases, Shawn leads the engineering of one of the most popular MLOps platforms for experiment tracking and model management.

Since co-founding W&B in 2017, he has built out its core tooling that enables researchers and teams (including OpenAI, Toyota, etc.) to version their models and visualize training results in real time. Shawn is an experienced software engineer (previously at Google and in startups) and an active open-source contributor in the W&B ecosystem. Under his technical direction, W&B’s developer tools have become a mainstay for reproducible machine learning projects.

Clement Delangue

Nationality: French

Co-founder & CEO of Hugging Face, Clément (a.k.a. “Clem”) has built one of the most influential open-source AI platforms.

Hugging Face is an open, collaborative hub where researchers share models, datasets, and MLOps best practices. Clem is a vocal advocate for open science and open-source in AI. Under his leadership (and active coding in early days of the HuggingFace Transformers library), the platform expanded beyond NLP to host computer vision and reinforcement learning models, becoming a de facto standard for model sharing and deployment.

He frequently speaks about democratizing ML technology and has fostered a global community of ML engineers.

Josh Tobin

Josh Tobin - Best 20 MLOps Engineers You Can Trust

Nationality: American

Co-founder & CEO of Gantry, Josh focuses on ML observability and post-deployment model monitoring.

A former OpenAI research scientist (and UC Berkeley PhD), he co-created the Full Stack Deep Learning course to teach engineers how to build and deploy ML systems end-to-end. At Gantry, he’s developing tools to track model performance and data quality in production, particularly for cutting-edge use cases like LLMs. Josh remains deeply technical – he’s known for promoting test-driven development for ML and has written about continuous learning in deployed models.

His blend of research (robotics, OpenAI) and practical MLOps experience makes him a thought leader in ML system reliability.

Valliappa Lakshmanan

Nationality: Indian

Global Head of Data Analytics & AI Solutions at Google Cloud, Lak is an author and hands-on architect who has helped hundreds of enterprises implement MLOps on Google’s platform.

He founded Google’s Advanced Solutions Lab ML Immersion program and has written influential books like “Machine Learning Design Patterns” and “Data Science on GCP”. Lak’s team builds software solutions using Google Cloud’s ML products, and he often shares best practices on deploying models, feature engineering, and pipeline automation on GCP.

With a blend of academic background and industry know-how, Lak has become a go-to expert for production ML guidance on cloud infrastructure.

Aparna Dhinakaran

Nationality: American

Co-founder & CPO of Arize AI, Aparna is a pioneer in ML observability and one of the youngest leaders in this space (Forbes 30 Under 30).

At Arize, she leads product development of a platform that helps enterprises detect drift, troubleshoot model issues, and analyze performance post-deployment. Aparna’s hands-on engineering background includes building core ML infrastructure at Uber – she was part of the Michelangelo platform team – as well as roles at Apple and TubeMogul. She frequently speaks at conferences on monitoring and improving AI fairness and reliability.

With her mix of big-tech experience and startup execution, Aparna has quickly become a thought leader in maintaining model quality in production.

Dean Pleban

Dean Pleban - Best 20 MLOps Engineers You Can Trust

Nationality: Israeli

Co-founder & CEO of DagsHub, Dean is an engineer-entrepreneur on a mission to bring open-source collaboration to data science and ML projects.

DagsHub provides a GitHub-like experience for versioning ML datasets, experiments, and code, integrating popular OSS tools under the hood. Dean’s multidisciplinary background (software engineering, ML research, physics, and even design) informs the product’s user-friendly approach. He also hosts the MLOps Podcast, where he interviews industry experts about getting models to production.

Julien Simon

Nationality: French

Currently Chief Evangelist at Hugging Face (formerly Global AI Evangelist at AWS), Julien is a tech influencer who bridges MLOps tools and practitioner communities.

With 6 years at Amazon Web Services focusing on AI/ML, he helped developers adopt cloud ML services and often demonstrated how to deploy and scale models on AWS infrastructure. Now at Hugging Face, Julien focuses on enabling open-source model deployment and training workflows (e.g., bringing HF models to AWS, Azure, etc.). He’s an active coder (sharing notebooks and demos on model optimization) and a frequent speaker/trainer worldwide.

His engaging content – from YouTube tutorials to blog posts – has inspired many engineers to experiment with new MLOps techniques.

Luigi Patruno

Nationality: American

Founder of MLinProduction, Luigi has become a respected educator and thought leader in the MLOps community. By day he leads a team of ML engineers as Senior Director of Data Science at 2U (building models & infrastructure to support student success).

By night (and weekends), he runs ML in Production, a blog and newsletter sharing best practices for running ML systems in the real world. Luigi’s writings (on topics like model deployment on AWS, Kubernetes for ML, model retraining, etc.) have guided many practitioners. He has also consulted for Fortune 500s on applied ML and taught graduate courses on data engineering.

His multi-faceted experience – spanning hands-on engineering, management, and teaching – gives him practical wisdom that he generously shares to help others avoid common production ML pitfalls.

Wrap Up

These legends represent exceptional talent, making them extremely challenging to headhunt. However, there are thousands of other highly skilled IT professionals available to hire with our help. Contact us, and we will be happy to discuss your hiring needs.

Note: We’ve dedicated significant time and effort to creating and verifying this curated list of top talent. However, if you believe a correction or addition is needed, feel free to reach out. We’ll gladly review and update the page.

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