Semi Senior Machine Learning Engineer

Muttdata
United States
RemoteCareer-pivot friendly

Who this role is best for

Strong fit for candidates who industrialize ML models and collaborate with cross-functional teams, with a focus on MLOps and cloud-based systems.

Best fit for

  • Candidates with hands-on MLOps experience and a track record of deploying models at scale
    — “experience deploying models to production at scale”
  • Individuals who have worked with Databricks and containerization technologies in production
    — “Hands-on experience with Databricks (Workflows, Jobs, Repos)”
  • Professionals comfortable with cloud platforms, especially Azure, and ML model governance
    — “Experience with Azure Cloud. Experience implementing model monitoring and observability practices.”

Things to consider

  • This role involves working with a multinational beverage company in Mexico City, requiring client alignment and cross-regional collaboration
    — “You’ll be working on impactful data and machine learning initiatives for a key client in the region.”
  • The role assumes a high level of technical ownership and responsibility for ML lifecycle management
    — “Strong technical ownership, attention to detail, and a passion for building reliable ML platforms are essential”

How to stand out

  • Highlight MLOps implementation and model deployment experience in your resume and interviews
    — “industrializing, deploying, monitoring, and scaling Machine Learning solutions in production”
  • Demonstrate expertise in building CI/CD pipelines and using MLflow for tracking and versioning
    — “Build and maintain CI/CD pipelines for ML workflows, ensuring smooth and reliable releases. Implement and manage model tracking, versioning, and registry using MLflow.”
  • Showcase your ability to work with Docker and Kubernetes in production environments
    — “Containerize ML applications with Docker and support deployment on Kubernetes-based infrastructure.”
  • Emphasize experience in aligning technical solutions with business stakeholders
    — “Collaborate closely with Data Scientists, Data Engineers, and business stakeholders to align technical solutions with business needs.”
  • Demonstrate your work with Spark / PySpark and cloud platforms like Azure
    — “Experience with Spark / PySpark. Experience with Azure Cloud.”
Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · Team

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

What success looks like

  • industrializing ml models
  • deploying and scaling ml solutions
  • collaborating with data scientists and engineers
Typical background
machine learningdata engineeringsoftware engineering

Skills & requirements

Required

PythonSQLSparkCi/cd PipelinesMlflowDockerKubernetes

Preferred

AWSDatabricks

Stack & domain

PythonSQLSparkCi/cd PipelinesGitMlflowDockerKubernetesLinux NetworkingClickhousePostgreSQLGrafanaLeadershipCommunicationTeamworkProblem-solvingAttention To DetailProject ManagementData AnalysisCustomer ServiceStrategic ThinkingInnovationFinanceMarketingE-commerceSupply ChainData ScienceMachine LearningCloud Computing

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 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!

Source: Muttdata careers (Lever)

Similar roles

More roles at Muttdata