Description:

The MLOps Engineer will design, automate, and manage robust machine learning pipelines that power AI-driven products.
This role involves connecting data scientists, machine learning engineers, and operations teams to ensure efficient deployment, monitoring, and maintenance of machine learning models.
Key responsibilities include building and automating ML pipelines, deploying models into production, optimizing model training and scalability, collaborating with data scientists and engineers, automating infrastructure provisioning, implementing monitoring and logging systems, and troubleshooting performance issues.

Requirements:

Strong experience with machine learning operations (MLOps) and DevOps practices, including CI/CD pipelines, containerization (Docker, Kubernetes), and infrastructure automation is required.
Expertise in cloud platforms such as AWS, GCP, or Azure, with experience in building scalable infrastructure for machine learning workloads is necessary.
Proficiency in scripting and programming languages like Python, Bash, and Terraform, along with hands-on experience with automation tools and machine learning libraries is essential.
Strong experience with monitoring tools like Prometheus, Grafana, or ELK Stack to track data pipelines and model performance in production environments is required.
Excellent communication skills and the ability to work closely with data scientists, software engineers, and IT teams are necessary.
A Bachelor’s or Master’s degree in Computer Science, Engineering, Machine Learning, or a related field is required, with equivalent experience in machine learning operations or DevOps being highly valued.
A minimum of 3+ years of experience in MLOps, DevOps, or cloud infrastructure management, with hands-on experience deploying machine learning models into production is required.

Comprehensive medical, dental, and vision insurance plans with low co-pays and premiums are provided.
Competitive vacation, sick leave, and 20 paid holidays per year are offered.
Flexible work schedules and telecommuting options promote work-life balance.
Opportunities for training, certification reimbursement, and career advancement programs are available for professional development.
Access to wellness programs, including gym memberships, health screenings, and mental health resources is provided.
Life insurance and short-term/long-term disability coverage are included.
Confidential counseling and support services through an Employee Assistance Program (EAP) are available.
Financial assistance for continuing education and professional development through tuition reimbursement is offered.
Opportunities to participate in community service and volunteer activities are encouraged.
Employee recognition programs celebrate achievements and milestones.