Senior MLOps Engineer

The MLOps Engineer role is crucial for advancing machine learning operations and development within business transformation initiatives across various sectors. This role focuses on developing and maintaining enterprise machine learning solutions, ensuring robust model governance, and maintaining high model performance standards. Responsibilities include setting and enforcing best practices for model deployment and managing the technical aspects of the machine learning lifecycle. The individual in this role will contribute significantly to stakeholder management, adopt a product development mindset, and help drive strategic transformation capabilities. The role involves collaborating with strategic partners to develop and maintain machine learning products, working with SaaS vendors to implement solutions, and supporting the realization of value from these solutions. The position also works closely with diverse stakeholders to support machine learning products, assist in defining roadmaps, contribute to project delivery and engagement, and facilitate the rollout and ongoing enhancement of solutions.

Responsibilities:

• Work with stakeholders to define machine learning solution designs based on cloud services such as Azure and Snowflake, adhering to industry best practices.
• Design and develop machine learning pipelines and frameworks to support enterprise analytical and reporting needs.
• Provide guidance and collaborate on model deployment design with data science, ML engineering, and data quality teams.
• Manage and configure cloud environments and related ML services.
• Implement improvements and new tools and approaches for model integration, storage, profiling, processing, management, and archival.
• Recommend platform improvements and development initiatives to meet strategic business and technology objectives.
• Utilize Agile practices to manage and deliver features.
• Stay current with key technologies and practices to assess suitability for business requirements.
• Collaborate with the Data Science Lead, Product Architect, and market-specific teams to define and support implementation of machine learning solutions aligned with business objectives.
• Provide hands-on technology expertise.
• Own deliverables, timelines, and quality standards.
• Co-own the product roadmap.
• Help support and optimize BAU processes.
• Perform other related accountabilities as assigned.

Requirements:

• Bachelor's degree in computer science, engineering, or a technical field preferred.
• Minimum 3-5 years of relevant experience.
• Proven experience in machine learning engineering and operations.
• Strong understanding of machine learning concepts, model lifecycle management, and model management capabilities, including model definitions, performance management, and integration.
• Experience executing model deployment, monitoring, profiling, governance, and analysis initiatives.
• Excellent interpersonal, oral, and written communication skills.
• Ability to relate ideas and concepts to others and write reports, business correspondence, project plans, and procedure documents.
• Solid Python programming skills and experience with ML frameworks such as TensorFlow and PyTorch.
• Strong data modeling and programming skills.
• Experience and strong understanding of cloud architecture and design across AWS, Azure, or GCP.
• Experience using modern approaches to automating machine learning pipelines.
• Familiarity with Agile and Waterfall methodologies.
• Ability to work independently and manage multiple task assignments within a structured implementation methodology.
• Strong commitment to continuous improvement and innovation.
• Self-motivated and able to work well with minimal supervision.
• Experience working across multiple teams and technologies.

Nice To Have:

• Experience with business intelligence tools, preferably Power BI.
• Experience with MLOps tools such as MLflow and Kubeflow.