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Machine Learning Engineer

Remote:

Full Remote

Contract:

Full time

Experience:

Mid-level (2-5 years)

Work from:

Canada, California (USA), United States

Offer summary

Qualifications:

Bachelor's or Master's in Computer Science, Engineering, or related field., 3+ years of experience in MLOps or DevOps., Strong programming skills in Python, GoLang, and familiarity with other languages., Experience with frameworks like TensorFlow, PyTorch, or scikit-learn., Proficiency with CI/CD tools and AWS..

Key responsabilities:

Architect for scalable, cost-efficient Client solutions.
Design and deploy solutions in AWS.
Build and maintain infrastructure for ML models.
Implement CI/CD pipelines and automate tasks.
Collaborate with data scientists and monitor model performance.

LanceSoft, Inc. XLarge https://www.lancesoft.com/

1001 - 5000 Employees

See more LanceSoft, Inc. offers

Job description

Title: MLOps Engineer
Location: Remote
Duration: 11 Months

We're seeking an experienced MLOps Engineer to lead the operationalization of our Machine Learning workloads. As a key team member, you'll be responsible for designing, building, and maintaining infrastructure required for efficient development, deployment, and monitoring of machine learning workloads. Your close collaboration with data scientists will ensure that our models are reliable, scalable, and performing optimally. This role requires expertise in automating Client workflows, enhancing model reproducibility, and ensuring continuous integration and delivery.

Responsibilities:

Architect for scalable, cost-efficient, reliable and secure Client solution.
Design, implement and deploy Client solutions in AWS.
Select and justify appropriate Client technology within AWS and Identify appropriate AWS services to implement Client solutions.
Design, build, and maintain infrastructure required for efficient development, deployment, and monitoring of machine learning models.
Implement CI/CD pipelines for machine learning applications to ensure smooth development and deployment processes.
Collaborate with data scientists to understand and implement requirements for model serving, versioning, and reproducibility.
Monitor and optimize model performance in production, identifying and resolving issues proactively to ensure optimal results.
Automate repetitive tasks to improve efficiency and reduce the risk of human error in MLOps workflows.
Maintain documentation and provide training to team members on MLOps best practices, ensuring knowledge sharing and collaboration within the team.
Stay updated with the latest developments in MLOps tools, technologies, and methodologies to remain current and effective in your role.

Qualifications:

Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
3+ years of experience in MLOps, DevOps, or related fields.
Strong programming skills in Python, GoLang with experience in other languages such as Java, C++, or Scala being a plus.
Experience with Client frameworks such as TensorFlow, PyTorch, and/or scikit-learn.
Proficiency with CI/CD tools such as Github Actions.
Hands-on experience with AWS.
Familiarity with containerization and orchestration tools like Docker and Kubernetes.
Knowledge of infrastructure-as-code tools such as AWS CDK and Cloudformation.
Strong understanding of machine learning lifecycle, including data preprocessing, model training, evaluation, and deployment.
Excellent problem-solving skills and the ability to work independently as well as part of a team.

Strong communication skills and the ability to explain complex technical concepts to non-technical stakeholders.

Preferred Qualifications:

AWS Certified Machine Learning – Specialty
Experience with feature stores, model registries, and monitoring tools such as MLflow, Tecton, or Seldon.
Familiarity with data engineering tools such as AWS EMR, Glue and Apache Spark.
Knowledge of security best practices for machine learning systems.
Experience with A/B testing and model performance monitoring.