Staff MLOps Engineer · Engineering Lead, MLOps · Author
I make machine learning land in production, and keep it running.
👋 I'm Prafful Mishra, a Staff MLOps & Platform Engineer based in Sweden. I operationalise ML by making sure the cool stuff lands in production and gets maintained, bridging the gap between ML practitioners, software engineers, and SREs.
🔭 Talk to me about
- Federated Learning
- Ethical AI
- MLOps
- Planes
- Cars
- Kung Fu Panda
- Big Hero 6
- Bruce Wayne
- Batman
- Gotham
About
// role I'm a Staff MLOps Engineer and engineering manager who is happiest at the seams of a system, where data, models, infrastructure, and the people building them all have to agree. I lead the MLOps engineering team at Epidemic Sound, building robust, maintainable ML platforms that let teams deploy and monitor models with confidence, and coaching the engineers who keep them running.
// 2025 In 2025 I published “A Guide to Implementing MLOps: From Data to Operations” with Springer, a practical walk through building end-to-end MLOps pipelines. I also write regularly on Medium and Substack about MLOps, platforms, and the messy realities of running ML in production.
// research I hold a Bachelors of Engineering (Honours) in Computer Science & Engineering from Rajiv Gandhi Prodyogiki Vishwavidyalaya, India. My research interests sit in Federated Learning and Quantum Computing.
Research publications
- Accuracy Crawler: An Accurate Crawler for deep web data harvesting
- A Probabilistic Weighted Ensemble Algorithm
Featured publication
A Guide to Implementing MLOps: From Data to Operations
A comprehensive, practical guide to building end-to-end MLOps pipelines, from data versioning through deployment to monitoring. Written for engineers who want production-grade ML without the hand-waving, drawn from real-world platform work.
Read on Springer →Work
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Staff MLOps Engineer · Engineering Lead
Leading the MLOps engineering team, and the platform it builds.
- Managing the team: weekly 1:1s, growth and learning plans, career coaching, and quarterly priorities.
- Running the goalie/on-call rotation, the hiring loop, and peer-team interview panels.
- Representing the team's roadmap to product, ML, and platform leadership.
- Leading the infrastructure for training foundational models and serving the product's GenAI inference.
- Architecting deployment strategies for ML products and optimising cost across ML initiatives.
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Senior MLOps Engineer
Drove the MLOps roadmap end to end.
- Defined the MLOps roadmap around the organisation's needs.
- Consulted ML engineers on development & production deployment strategy.
- Built and maintained training/serving infrastructure and data pipelines.
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Machine Learning Engineer
Core member of the central ML Engineering & Operations team.
- Built and ran a Kubernetes-native, multi-cluster central data science platform used across the org.
- Consulted teams across the full lifecycle, from data collection to scalable deployments.
- Unblocked teams to minimise time-to-production for models.
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Machine Learning Engineer
- Developed, deployed, and maintained ML models across distributed architectures with Kubernetes, Kubeflow, PyTorch, and AWS.
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Senior Software Developer, Machine Learning
- Researched and helped design architecture for ML solutions within the Applied R&D team.
Stack
The tools and ideas I reach for most.
MLOps & Infrastructure
Machine Learning
Programming & Data
Exploring
Open source
I contribute back to the MLOps and infrastructure tools I build on.
lakeFS
Git-like version control for object storage and data lakes.
View on GitHub → k8sKubeflow
The machine learning toolkit for Kubernetes.
View on GitHub → dagKubeflow Pipelines
Building and deploying portable, scalable ML workflows.
View on GitHub → pyKubernetes Python Client
The official Python client library for the Kubernetes API.
View on GitHub →Writing
Notes from the field, on Medium & Substack.
How to Version Unstructured Data?
Strategies and tooling for versioning unstructured data inside ML pipelines.
Read → May 2023Cracking the Code of Data Science Team Structures
How to build effective data science teams and the org structures around them.
Read → Apr 2023Why Being a Software Engineer is a Competitive Sport
Reflections on the competitive nature of engineering and continuous learning.
Read → Jul 2022Deleting my First Production Cluster
Lessons from managing production infra and why careful operations matter.
Read → Feb 2021Conditional Kubeflow Pipeline for Dummies
A beginner-friendly guide to building conditional pipelines in Kubeflow.
Read → Nov 2019A Dummies' Guide to Build a Kubeflow Pipeline
A step-by-step tutorial on creating your first Kubeflow pipeline from scratch.
Read →Get in touch
Always happy to talk MLOps, platforms, responsible AI, or Kung Fu Panda 🐼. Have a project, a question, or just want to say hi?
hello@mishraprafful.com