MLOps Engineer

Sasso Consulting (Pty) Ltd·Remote(United States)
Data & Analytics
Excel

WFA Digital Insight

The demand for skilled MLOps Engineers has skyrocketed, with a 25% increase in job postings in the last year alone. As companies continue to invest in AI and machine learning, professionals with expertise in deploying and maintaining ML models are in high demand. Sasso Consulting's commitment to innovation and remote work makes this role particularly attractive. With the global shift towards remote work, candidates can expect a flexible and collaborative environment. Before applying, candidates should be aware of the evolving landscape of MLOps and the need for continuous learning in this field.

Job Description

About the Role

The MLOps Engineer role is a critical component of Sasso Consulting's growing team, focusing on the deployment, monitoring, and maintenance of machine learning models in production environments. This position requires a unique blend of machine learning, DevOps, and software engineering skills to ensure the seamless integration of AI models into business systems. The successful candidate will work at the forefront of machine learning, collaborating closely with data scientists and engineers to productionize models and ensure their reliability, scalability, and performance.

As an MLOps Engineer, you will be responsible for designing, building, and maintaining end-to-end ML pipelines, deploying machine learning models into production environments, and implementing robust CI/CD pipelines for ML workflows. Your work will directly impact the company's ability to leverage AI and machine learning to drive business growth and innovation.

The role is fully remote, offering flexibility and the opportunity to work with a global team. Sasso Consulting values collaboration, innovation, and excellence, providing a dynamic environment for professionals to grow and develop their skills.

What You Will Do

  • Design, build, and maintain end-to-end ML pipelines
  • Deploy machine learning models into production environments
  • Implement and manage CI/CD pipelines for ML workflows
  • Monitor model performance, data drift, and system health
  • Automate model retraining and versioning processes
  • Collaborate with Data Scientists and Engineers to productionize models
  • Ensure scalability, reliability, and security of ML systems
  • Manage cloud infrastructure for ML workloads (AWS, Azure, or GCP)
  • Troubleshoot and resolve issues in production ML systems

What We Are Looking For

  • Strong experience in MLOps or DevOps within ML environments
  • Proficiency in Python and scripting for automation
  • Experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn)
  • Hands-on experience with CI/CD tools (GitHub Actions, Jenkins, GitLab CI)
  • Knowledge of containerization (Docker) and orchestration (Kubernetes)
  • Experience with cloud platforms (AWS, Azure, or GCP)
  • Familiarity with model monitoring tools (e.g., Prometheus, MLflow, Evidently)
  • Understanding of data pipelines and ETL processes
  • Experience with version control systems (Git)

Nice to Have

  • Experience with Kubeflow, Airflow, or SageMaker
  • Knowledge of infrastructure as code (Terraform, CloudFormation)
  • Exposure to feature stores and model registries
  • Experience with real-time/streaming data systems (Kafka, Spark)

Benefits and Perks

  • Fully remote work environment
  • Flexible working hours
  • Opportunity to work on cutting-edge AI/ML systems
  • Collaborative and innovative team culture
  • Competitive salary and benefits
  • Professional development opportunities
  • Access to the latest tools and technologies
  • Recognition and rewards for outstanding performance

How to Stand Out

  • Ensure your portfolio includes examples of deploying and managing machine learning models in production environments, highlighting any experience with CI/CD pipelines and automation.
  • Familiarize yourself with the latest trends and tools in MLOps, including Kubeflow, Airflow, and SageMaker, to stand out as a candidate.
  • Practice explaining complex technical concepts in simple terms, as strong communication skills are crucial for this role.
  • Be prepared to discuss your experience with cloud platforms, containerization, and orchestration, and how you've applied these skills in previous roles.
  • Consider earning certifications in ML frameworks or cloud platforms to enhance your application and demonstrate your expertise.
  • Tailor your resume and cover letter to highlight your experience with machine learning, DevOps, and software engineering, and be specific about your achievements in these areas.

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