Prinicipal MlOps Engineer

Job Title: Principal MLOps Engineer
Location: Sunnyvale, CA
Job Type: - Contract - 12+ Months
Department: Data Science / Machine Learning

About the Role
We are seeking an experienced Principal MLOps Engineer to lead and scale our machine
learning operations, ensuring eJicient, secure, and reliable ML model deployments. As a


senior technical leader, you will be responsible for designing and implementing a cutting-
edge MLOps framework, driving automation, and enhancing ML infrastructure to support


large-scale, mission-critical applications. This role requires deep expertise in MLOps best
practices, cloud architecture, and DevOps principles, along with strong leadership and
collaboration skills to guide engineering teams and stakeholders.

Key Responsibilities

• Architect and lead the development of scalable and robust ML infrastructure to support the entire model lifecycle, from experimentation to production.
• Establish MLOps best practices, ensuring automation, reproducibility, versioning, and monitoring of models in production.
• Design and implement CI/CD pipelines for machine learning models, integrating security, compliance, and performance optimization.
• Drive ML observability strategies, implementing monitoring tools for detecting model drift, data drift, and performance degradation.
• Optimize and manage cloud-based ML workloads using AWS, GCP, or Azure, ensuring cost-eJiciency and scalability.
• Lead and mentor a team of MLOps engineers, collaborating closely with data scientists, software engineers, and DevOps teams.
• Define infrastructure as code (IaC) using Terraform, Kubernetes, and containerization tools to standardize deployments.
• Enhance ML model serving architectures, leveraging Kubernetes, serverless computing, or specialized model-serving frameworks.
• Implement robust security frameworks for ML workflows, ensuring data privacy, access control, and compliance with industry regulations.
• Stay ahead of industry trends, evaluating and integrating new technologies to improve automation and eJiciency in ML workflows.

Requirements

Technical Skills

• Expertise in Python and experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn, etc.).
• Deep knowledge of CI/CD pipelines, DevOps practices, and cloud-native architectures.
• Strong experience with Kubernetes, Docker, and infrastructure-as-code tools (Terraform, Ansible, etc.).
• Advanced understanding of ML pipeline orchestration tools like Kubeflow, MLflow, Airflow, or TFX.
• Proficiency in monitoring and observability tools like Prometheus, Grafana, ELK Stack, or Datadog for ML workloads.
• Experience with distributed computing frameworks (e.g., Spark, Ray, Dask) is a plus.
• Familiarity with model explainability, fairness, and bias detection tools is highly desirable.
• Strong knowledge of security best practices for ML systems, including data encryption, API security, and governance.

Soft Skills

• Proven leadership in architecting, deploying, and managing large-scale M infrastructure.
• Strong ability to mentor and lead teams, fostering best practices and knowledge sharing.
• Excellent problem-solving and critical thinking skills to tackle complex ML engineering challenges.
• EJective communication and collaboration with cross-functional teams, including engineering, product, and business stakeholders.

Education & Experience

• Bachelor's or master's degree in computer science, Machine Learning, Data Engineering, or a related field.
• 7+ years of experience in MLOps, DevOps, or ML infrastructure engineering.
• Proven track record of leading ML deployment initiatives at scale in enterprise or high growth environments.