Semi Senior Machine Learning Engineer
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Summary
Semi-senior ML engineer at Mutt Data, a remote ML/data consultancy, embedded on a project for a multinational beverage client in Mexico City. Day to day: industrializing, deploying, monitoring, and scaling ML models in production using Python, Spark, MLflow, Databricks, Docker/Kubernetes, and Azure.
🚀 Join Our Data Products and Machine Learning Development Remote Startup! 🚀
Mutt Data is a dynamic startup committed to crafting innovative systems using cutting-edge Big Data and Machine Learning technologies.
We’re looking for a Semi Senior Machine Learning Engineer to help take our expertise to the next level. If you consider yourself a data nerd like us, we’d love to connect! 🐶🚀
This opportunity is with a leading multinational beverage company based in Mexico City. You’ll be working on impactful data and machine learning initiatives for a key client in the region.
You'll be responsible for industrializing, deploying, monitoring, and scaling Machine Learning solutions in production, ensuring MLOps best practices, traceability, reliability, and operational excellence across the full model lifecycle. This role works closely with Data Scientists, Data Engineers, and business stakeholders, playing a key role in turning ML models into robust, production-grade systems. Strong technical ownership, attention to detail, and a passion for building reliable ML platforms are essential to succeed in this fast-paced, collaborative environment.
🚀 What We Do
- Leveraging our expertise, we build modern Machine Learning systems for demand planning and budget forecasting.
- Developing scalable data infrastructures, we enhance high-level decision-making, tailored to each client.
- Offering comprehensive Data Engineering and custom AI solutions, we optimize cloud-based systems.
- Using Generative AI, we help e-commerce platforms and retailers create higher-quality ads, faster.
- Building deep learning models, we enhance visual recognition and automation for various industries, improving product categorization, quality control, and information retrieval.
- Developing recommendation models, we personalize user experiences in e-commerce, streaming, and digital platforms, driving engagement and conversions.
🌟 Our Partnerships
- Amazon Web Services
- Astronomer
- Databricks
🌟 Our Values
- 📊 We are Data Nerds
- 🤗 We are Open Team Players
- 🚀 We Take Ownership
- 🌟 We Have a Positive Mindset
🔍 Curious about what we’re up to? Check out our case studies and dive into our blog post to learn more about our culture and the exciting projects we’re working on! 🚀
Responsibilities 🤓
- Industrialize, deploy, and scale Machine Learning models into production environments.
- Design and maintain training, inference, and retraining pipelines end-to-end.
- Build and maintain CI/CD pipelines for ML workflows, ensuring smooth and reliable releases. Implement and manage model tracking, versioning, and registry using MLflow.
- Develop and expose APIs for model serving, ensuring performance and scalability.
- Orchestrate workflows and jobs on Databricks (Workflows, Jobs, Repos).
- Containerize ML applications with Docker and support deployment on Kubernetes-based infrastructure. Implement model governance and versioning practices to ensure traceability across the ML lifecycle.
- Collaborate closely with Data Scientists, Data Engineers, and business stakeholders to align technical solutions with business needs.
- Promote MLOps best practices and modern ML architecture across the team.
Required Skills
- Advanced Python and SQL.
- Experience with Spark / PySpark.
- Solid experience with CI/CD pipelines and Git.
- Experience with MLflow (tracking, registry, and deployment).
- Experience with Docker and working knowledge of Kubernetes concepts.
- Experience with Azure Cloud.
- Experience implementing model monitoring and observability practices.
- Strong understanding of MLOps and ML architecture principles.
- Experience deploying models to production at scale.
Nice to Have Skills 😉
- Hands-on experience with Databricks (Workflows, Jobs, Repos).
- Experience with other cloud providers (AWS, GCP)
- Experience with Kubernetes in production environments.
🎁 Perks
- Remote-first culture – work from anywhere! 🌍
- AWS, DBT, Google Cloud, Azure & Databricks certifications fully covered
- Birthday off + an extra vacation week (Mutt Week! 🏖️)
- Referral bonuses – help us grow the team & get rewarded!
- Maslow: Monthly credits to spend in our benefits marketplace.
- ✈️🏝️ Annual Mutters' Trip – an unforgettable getaway with the team!
Skills
As published by lever · 11 questions · 7 written answers
Basics
Resume/CV, Full name, Email, Phone, Current location, Current company, LinkedIn URL, GitHub URL, Other URL
Pick from a list (4)
- Do you currently reside in Mexico or Latin America? (This is a mandatory requirement for this role. If you do not currently reside in Latin America or Mexico, your application will not be considered.)
- Where did you hear about us? optional
- English Level* optional
- Working mode optional
Written answers (7)
- Describe about your relevant experience in the role optional
- Have you deployed ML models to production at scale? optional
- Have you worked with Databricks (Workflows, Jobs, Repos)? optional
- Have you worked with Spark/PySpark? optional
- Do you have experience with Docker and Kubernetes concepts? optional
- Study/Degree optional
- Gross net salary expectations (USD) optional
