Principal MLOps Engineer

TriNet is a leading provider of comprehensive human resources solutions for small to midsize businesses (SMBs). We enhance business productivity by enabling our clients to outsource their HR function to one strategic partner and allowing them to focus on operating and growing their core businesses. Our full-service HR solutions include features such as payroll processing, human capital consulting, employment law compliance and employee benefits, including health insurance, retirement plans and workers’ compensation insurance. 

TriNet has a nationwide presence and an experienced executive team. Our stock is publicly traded on the NYSE under the ticker symbol TNET. If you’re passionate about innovation and making an impact on the large SMB market, come join us as we power our clients’ business success with extraordinary HR.

Don't meet every single requirement? Studies have shown that women and people of color are less likely to apply to jobs unless they meet every single requirement. At TriNet, we are dedicated to building a diverse, inclusive and authentic workplace, so if you're excited about this role but your past experience doesn't align perfectly with every single qualification in the job description, we encourage you to apply anyways. You may just be the right candidate for this or other roles. 

JOB SUMMARY
As a Principal MLOps Engineer, you will play a pivotal role in designing, implementing, and optimizing machine learning operations within our infrastructure. You will collaborate closely with data scientists, software engineers, and other cross-functional teams to ensure the seamless deployment and maintenance of machine learning models.

Essential Duties/Responsibilities
•   Develop, manage, and scale the infrastructure required for efficient and reliable machine learning model deployment, including orchestration, version control, and monitoring systems.
•   Design and implement automation pipelines for model training, testing, validation, and deployment, integrating CI/CD practices to ensure smooth and consistent delivery of ML models.
•   Deploy machine learning models into production environments, establishing monitoring and alerting systems to track model performance, health, and data drift. Proactively troubleshoot issues and optimize model efficiency.
•   Collaborate with data scientists, software engineers, and DevOps teams to streamline the integration of machine learning solutions into existing software systems and workflows.
•   Ensure adherence to security protocols and compliance standards throughout the machine learning lifecycle, implementing best practices for data privacy and model governance.
•   Stay updated with the latest advancements in MLOps tools, technologies, and methodologies, and propose enhancements to existing processes to improve efficiency and reliability.
•   Mentors and supports other members of the Enterprise Data team and contributes to the software development best practices  
•   Serves as an expert advisor to executives on the use of current and future technologies for optimal impact on current and future business strategies and results 
•   Leads innovation across the organization by exploring new technologies, demonstrating how to leverage those discoveries through proof of concept and incorporating them into the platform

Required for All Jobs
•   Performs other duties as assigned
•   Complies with all policies and standards

QUALIFICATIONS
Education Level
Bachelor's Degree in Computer Science/Engineering or equivalent experience

Experience   
Typically 12+ years   Proven experience working in a similar MLOps or DevOps role within a technical engineering environment.
Typically 12+ years   Proficiency in programming languages such as Python, Java, or similar, along with experience with relevant ML frameworks (TensorFlow, PyTorch, etc.).
Experience with CI/CD pipelines, version control systems (Git), and automation tools.

KSAs
Knowledge of end-to-end SDLC process in EDW, Data Lake, BI & MLOps projects
Dimensional Modeling and Data warehouse, ODS concepts, such as star schemas, snowflakes, and normalized data models
Strong knowledge of containerization technologies (Docker, Kubernetes) and cloud platforms (AWS, Azure, GCP).
Ability to coordinate effectively with on-site and offshore resources through Managed Service Providers & IT Teams
Solid understanding of machine learning concepts, model development, and deployment strategies.
Knowledge of Reporting tools like Tableau is desired
Excellent verbal and written communication skills
Excellent problem-solving skills and ability to work in a fast-paced, collaborative environment.
Experience in drafting best coding practi