Machine Learning Operations Engineer

Shuru is a self-managed technology team specializing in accelerating visions through product, technology, and AI leadership. They are hiring an MLOps Engineer to build, deploy, and maintain enterprise machine learning solutions, focusing on scalable ML pipelines and cloud-based environments.

Responsibilities:

• Work with stakeholders to define machine learning solution designs based on cloud services such as Azure and Snowflake
• Design, build, and maintain machine learning pipelines and frameworks to support enterprise analytics, reporting, and product needs
• Collaborate with data science, ML engineering, data quality, and product teams on model deployment architecture and implementation
• Manage, configure, and optimize cloud environments and related machine learning services
• Implement tools and processes for model integration, storage, profiling, monitoring, processing, management, and archival
• Support enterprise-wide model governance, performance tracking, and lifecycle management standards
• Recommend improvements to ML platforms, tools, and development practices to support strategic technology and business objectives
• Work with SaaS vendors and strategic partners to implement and maintain modern machine learning solutions
• Use Agile practices to manage delivery, contribute to project planning, and support successful rollout of ML products
• Partner with internal stakeholders to understand business requirements and translate them into reliable ML operations solutions
• Stay current with emerging MLOps tools, cloud technologies, and best practices to ensure solutions remain scalable, secure, and fit for purpose

Requirements:

• Bachelor's degree in Computer Science, Engineering or Technical Field preferred
• Minimum 3-7 years of relevant experience
• Proven experience in machine learning engineering and operations
• Profound understanding of machine learning concepts, model lifecycle management, and experience in model management capabilities including model definitions, performance management and integration
• Execution of model deployment, monitoring, profiling, governance and analysis initiatives
• Excellent interpersonal, oral, and written communication; Ability to relate ideas and concepts to others; write reports, business correspondence, project plans and procedure documents
• Solid Python, ML frameworks (e.g., TensorFlow, PyTorch), data modeling, and programming skills
• Experience and strong understanding of cloud architecture and design (AWS, Azure, GCP)
• Experience using modern approaches to automating machine learning pipelines
• Agile and Waterfall methodologies
• Ability to work independently and manage multiple task assignments within a structured implementation methodology
• Personally invested in continuous improvement and innovation
• Motivated, self-directed individual that works well with minimal supervision
• Must have experience working across multiple teams/technologies
• Experience with business intelligence tools (preferably PowerBI)
• Experience with MLOps tools (e.g., MLflow, Kubeflow)