Senior ML Ops Engineer

Key Skills

Aartificial intelligence, Continuous Integration & Continuous Delivery - CI/CD, Data Analytics, Data pipelines, Data science techniques, DevOps, Docker, Github, IT, Kubernetes-K8s, Machine learning techniques, MLOps tools, Teamwork

Job Description

As ML Ops Engineer, you will work with Data Scientists and Data Architects to translate prototypes into scalable solutions. You will build, deploy, run and monitor ML & AI solutions bridging the gap between Data Scientists and Operations. You will ensure that models conform to the ML strategy and guidance established. You should be proficient at building, training, deploying, and monitoring ML models.

Scope/Categories: 

• Role will report to the ML Ops Engineer Manager.

• Travel may include approximately 15% of work time.

Key Interfaces

• Internal: Data Science Team, Data & Analytics Team, Data & Analytics Product Managers and Product Teams, D&A COE, Cloud COE, UX Designers, Web Apps Developers, Enterprise Architecture, Cyber Security Team, Legal Team.

• External: Contractors, Consulting Partners, 3rd Party service providers.

Main Responsibilities: 

• As a Senior ML OPS Engineer, you’ll be part of the NA Data Science lean software team dedicated to productionizing machine learning applications and systems at scale.

• You’ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms.

• You’ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications.

• You’ll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering.

• As a Senior ML OPS Engineer, you will be joining a team of experienced Machine Learning Engineers that support, build, and enable Machine Learning capabilities across the NA organization.

• You’ll work closely with internal customers, data & analytics, and cloud team to build our next generation data science workbench and ML platform and products.

• You’ll be able to further expand your knowledge and develop your expertise in modern Machine Learning frameworks, libraries and technologies while working closely with internal stakeholders to understand the evolving business needs.

• Implement scalable and reliable systems leveraging cloud-based architectures, technologies and platforms to handle model inference at scale.

• Deploy and manage machine learning & data pipelines in production environments.

• Work on containerization and orchestration solutions for model deployment.

• Participate in fast iteration cycles, adapting to evolving project requirements.

• Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications.

• Leverage CICD best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.

• Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI.

• Collaborate with Data scientists, software engineers, data engineers, and other stakeholders to develop and implement best practices for MLOps, including CI/CD pipelines, version control, model versioning, monitoring, alerting and automated model deployment.

• Manage and monitor machine learning infrastructure, ensuring high availability and performance.

• Implement robust monitoring and logging solutions for tracking model performance and system health.

• Monitor real-time performance of deployed models, analyze performance data, and proactively identify and address performance issues to ensure optimal model performance.

• Troubleshoot and resolve production issues related to ML model deployment, performance, and scalability in a timely and efficient manner.

• Implement security best practices for machine learning systems and ensure compliance with data protection and privacy regulations.

• Collaborate with platform engineers to effectively manage cloud compute resources for ML model deployment, monitoring, and performance optimization.

• Develop and maintain documentation, standard operating procedures, and guidelines related to MLOps processes, tools, and best practices.

Key Qualifications and Experiences:

• Bachelor’s degree in management information systems/technology, Computer Science, Engineering, or related discipline. MBA or equivalent is preferred.

• 10+ years of experience as part of large, remote, global IT teams. 7+ years focused on development lifecycle product architecture design.

• 5+ years proven experience in an engineering role with a focus on MLOps, Data Engineering, ML Engineering.

• 5+ years of experience in ML Lifecycle using Azure Kubernetes service, Azure Container Instance service, Azure Data Factory, Azure Monitor, Azure DataBricks building datasets, ML pipelines, experiments, logging, and monitoring. (Including Drifting, Model Adaptation and Data Collection).

• 5+ years of experience in an execution role engaging with Data Scientists to deliver large scale analytics solutions and projects.

• 5+ years of experience in data engineering using Snowflake.

• Knowledge of machine learning model training, building, algorithm selection and interpretability.

• Experience in designing, developing & scaling complex data & feature pipelines feeding ML models and evaluating their performance

• Experience in building and managing streaming and batch inferencing.

• Proficiency in SQL and any one other programming language (e.g., R, Python, C++, Minitab, SAS, Matlab, VBA – knowledge of optimization engines such as CPLEX or Gurobi is a plus)

• Strong experience with cloud platforms (AWS, Azure, etc.) and containerization technologies (Docker, Kubernetes)

• Experience with CI/CD tools such as GitHub Actions, GitLab, Jenkins, or similar tools.

• Experience with ML frameworks and libraries (TensorFlow, PyTorch, Scikit-learn).

• Familiarity with security best practices in DevOps and ML Ops.

• Experience in developing and maintaining APIs (e.g.: REST)

• Agile/Scrum operating experience using Azure DevOps.

• Experience with MS Cloud - ML Azure Databricks, Data Factory, Synapse, among others.