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Machine Learning Ops

Remote: 
Full Remote
Contract: 
Experience: 
Mid-level (2-5 years)
Work from: 

Offer summary

Qualifications:

Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or related field., 3+ years of experience in machine learning operations or model deployment., Expertise in programming languages such as Python or R and familiarity with ML frameworks like TensorFlow or PyTorch., Strong skills in version control (e.g. Git), CI/CD processes, and containerization technologies like Docker..

Key responsabilities:

  • Design, develop, and implement scalable pipelines for data and model deployment in production environments.
  • Monitor and maintain the performance of deployed models, ensuring they meet accuracy and robustness standards.
  • Automate ML workflow processes for continuous integration and delivery related to ML models.
  • Collaborate with data scientists to transition models from development to production and ensure compliance with data privacy regulations.

Foxbox Digital logo
Foxbox Digital Scaleup https://www.foxbox.com/
51 - 200 Employees
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Job description

Foxbox Digital is an award-winning digital product agency, headquartered in Chicago. We partner with clients ranging from start-ups to mid-sized businesses and everyone in between to design, develop, and deliver successful digital experiences.

We're a remote-first team of associates located in the United States and LATAM regions. Our mission is rooted in continuously engaging and assembling tech-enthusiasts together to build our global team.

Summary

As an MLOps Engineer at Foxbox Digital, you will play a pivotal role in operationalizing ML models for our client engagements. Your responsibilities will include deploying, monitoring, and retraining models as needed, with a strong focus on CI/CD automation and scalability. You will work closely with data scientists, software engineers, and product teams to deliver AI/ML solutions that meet or exceed client objectives.

Responsibilities:

  • Design, develop, and implement scalable pipelines for data and model deployment in production environments.
  • Monitor and maintain the performance of deployed models, ensuring they meet specified accuracy and robustness standards.
  • Automate ML Workflow processes for continuous integration and continuous delivery (CI/CD) related to ML models.
  • Collaborate with data scientists to transition models from development to production
  • Ensure compliance with data privacy regulations and best practices in machine learning operations.
  • Develop tools for monitoring and visualizing model performance and data drift.
  • Conduct regular audits of ML systems to identify improvement opportunities.
  • Train team members on best practices for deploying and maintaining machine learning models.

Requirements

Who You are:

  • You hold a Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or related field.
  • You have 3+ years of experience in machine learning operations, model deployment, or related experience.
  • You have expertise in programming languages such as Python or R.
  • You are familiar with ML frameworks (e.g. TensorFlow, PyTorch, Scikit-learn).
  • You have experience in cloud technologies like AWS, Google Cloud, or Azure.
  • You possess strong skills in version control (e.g. Git) and CI/CD processes.
  • You have a solid understanding of containerization technologies such as Docker and orchestration systems like Kubernetes.
  • You have adept problem-solving skills and the ability to work collaboratively in cross-functional teams.
  • You have excellent written and verbal communication skills to convey complex technical concepts clearly.
  • You have a proactive attitude towards learning and adapting to new technologies.
  • Experience with MLOps or tools such as MLflow, Kubeflow, or Airflow is a plus.
  • Knowledge of data engineering and ETL processes is an advantage.

Benefits

Technologies we use:
  • ML Frameworks: TensorFlow, PyTorch, scikit-learn – for training, experimenting, and deploying various machine learning models.
  • MLOps & Workflow Orchestration: Kubeflow, MLflow, Airflow – to manage model versioning, pipelines, and experiments.
  • Cloud Platforms: AWS, Azure, GCP – for scalable compute resources, managed AI services, and secure data storage.
  • Containerization & Orchestration: Docker, Kubernetes – to package models and applications for consistent, flexible deployment.
  • Data Engineering & Integration: Apache Spark, Kafka, or Azure Data Factory – for building robust data pipelines and real-time data processing.
  • Programming Languages: Python, R – primarily for ML model development, data analysis, and scripting.
Why Foxbox Digital
  • We offer continuous training and growth opportunities
  • Remote-first environment with a culture of collaboration and innovation.
  • Opportunity to work on a project that directly impacts business success.
  • You are part of a multicultural and collaborative team that is constantly growing.
  • Don’t be afraid to break things; we encourage risk-takers.
Diversity and Inclusion

Foxbox Digital is an LGBT company certified by the Illinois and National LGBT Chambers of Commerce. We are committed to working with diverse and inclusive teams to continue building the digital revolution.

Foxbox is committed to the principle of equal employment opportunity for all and team members with a work environment free of discrimination and harassment. All employment decisions at Foxbox are based on business needs, job requirements and individual qualifications, without regard to race, color, religion or belief, family or parental status, or any other status protected by the laws or regulations in the locations where we operate. Foxbox will not tolerate discrimination or harassment based on any of these characteristics. Foxbox encourages applicants of all ages.

Required profile

Experience

Level of experience: Mid-level (2-5 years)
Spoken language(s):
English
Check out the description to know which languages are mandatory.

Other Skills

  • Adaptability
  • Collaboration
  • Communication
  • Problem Solving

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