MLOps Engineer Senior

Muttdata
Worldwide Remote
Remote

Who this role is best for

A natural match if you have experience with ML deployment and cloud infrastructure.

Best fit for

  • Candidates with a background in deploying ML models at scale and managing cloud-based infrastructure.
    — “Experience deploying models to production at scale.”
  • Individuals who have worked with Databricks and Kubernetes in production environments.
    — “Experience with Docker and working knowledge of Kubernetes concepts.”
  • Professionals who have built and maintained CI/CD pipelines for ML workflows using Git and MLflow.
    — “Build and maintain CI/CD pipelines for ML workflows, ensuring smooth and reliable releases. Implement and manage model tracking, versioning, and registry using MLflow.”

Things to consider

  • This role requires working with Azure Cloud as a mandatory technical commitment.
    — “Experience with Azure Cloud.”
  • The position demands a high level of collaboration across multiple technical and business teams.
    — “Collaborate closely with Data Scientists, Data Engineers, and business stakeholders to align technical solutions with business needs.”

How to stand out

  • Highlight your experience with full ML lifecycle management in your resume and interview responses.
    — “You'll be responsible for industrializing, deploying, monitoring, and scaling Machine Learning solutions in production.”
  • Demonstrate technical leadership in MLOps practices and model governance through specific examples.
    — “Implement model governance and versioning practices to ensure traceability across the ML lifecycle.”
  • Showcase your ability to build and maintain APIs for model serving with a focus on performance and scalability.
    — “Develop and expose APIs for model serving, ensuring performance and scalability.”
Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · TeamLevel · Senior

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • Industrializing, deploying, and scaling Machine Learning models into production environments
  • Designing and maintaining training, inference, and retraining pipelines end-to-end
  • Developing and exposing APIs for model serving
Typical background
Advanced degree in Computer Science, Data Science, or related fieldExperience in a startup environment

Skills & requirements

Required

PythonSQLSpark / PysparkCi/cd PipelinesGitMlflowDockerKubernetesAzure CloudMlopsML Architecture Principles

Preferred

Experience With Databricks

Stack & domain

PythonSQLSparkPysparkCi/cd PipelinesGitMlflowDockerKubernetesAzure CloudModel MonitoringMlopsML ArchitectureLeadershipAttention To DetailCollaborationProblem-solvingData ProductsMachine LearningBig DataCloud-based SystemsGenerative AIDeep LearningRecommendation Models

About the role

Original posting from Muttdata via Lever

🚀 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 MLOps Engineer Senior to help take our expertise to the next level. If you consider yourself a data nerd like us, we’d love to connect! 🐶🚀

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!
  • 🚀 In-Company English Lessons.
  • 💪 Wellhub or sports club stipend to stay active
  • 🚀 AWS, DBT, Google Cloud, Azure & Databricks certifications fully covered
  • 🍕 Food credits via Pedidos Ya – because great work deserves great food.
  • 🎂 Birthday off + an extra vacation week (Mutt Week! 🏖️)
  • 🤝 Referral bonuses – help us grow the team & get rewarded!
  • ✈️🏝️ Annual Mutters' Trip – an unforgettable getaway with the team!
  • 👶 Monthly Childcare Reimbursement  – Because supporting families matters too

Source: Muttdata careers (Lever)

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