Senior Machine Learning Engineer

Sherwin-Williams•Cleveland, OH

12d•$108,531 - $140,086•Onsite

About The Position

The Senior Machine Learning Engineer is responsible for designing, developing, testing and operationalizing ML (Machine Learning) and other data solutions, including cloud-based or internet-related tools for internal or external consumption. The incumbent will provide technical solutions to enable the company to meet its strategic objectives through effective collaboration with business analysts, data scientists, software engineers, platform engineers, quality assurance testers, and project managers. The incumbent will collaborate with others to ensure successful teamwork, effective development, and delivery of high-quality machine learning and data solutions that include MLOps practices such as CI/CD pipelines, automated testing, and monitoring.

Requirements

Bachelor's Degree in Computer Science or Computer Engineering or in lieu of a degree, at least 7 years of experience in software development
4+ years of experience in ML, MLOps, and/or software development
In-depth knowledge of software development life cycle (SDLC) methodologies and practices, particularly MLOps and DevOps
In-depth knowledge of software development tools, platforms and languages, particularly cloud platforms
Experience in developing, integrating, testing and deploying software solutions for cloud-based or internet-related tools
Knowledge of relational databases and SQL
Excellent written and verbal communication skills
Experience in developing Microservices and in-depth knowledge of various Integration patterns
Must be eighteen years or older
Must be legally authorized to work in the United States without company sponsorship

Nice To Haves

Databricks experience
Supply chain experience
R&D experience
AI implementation experience be it RAG/TAG/other generative or agentic workloads
SW experience preferred

Responsibilities

Design, develop, and deploy machine learning models and other data solutions in alignment with business requirements and technical specifications defined
Train, validate, and optimize machine learning models and other data solutions to ensure high performance and accuracy. Implement techniques for model/solution improvement and fine-tuning.
Write high-quality, maintainable, and scalable code
Create and maintain technical documentation including model development processes, deployment workflows, operational procedures, code comments, design documents, and user manuals
MLOps Implementation: Develop and maintain CI/CD pipelines for seamless model deployment. Drive testing throughout the development, integration, deployment, and management of the software solution, and automating where possible to ensure solution quality and security in accordance with industry standards and company policies
Collaborate with data scientists, business analysts, software engineers, IT operations, quality assurance testers, and project managers to to translate business requirements into successful technical solutions that are integrated into production environments
Provide ongoing solution support including incident and problem management, root cause analysis, request fulfilment, security compliance, fault repair, resiliency testing, and observability
Innovation and Research: Stay updated with the latest advancements in machine learning and MLOps practices. Explore and implement innovative solutions to enhance the company’s machine learning capabilities.