Lead AI Engineer

Sherwin-Williams; Cleveland (OH); Remote; USD 140,000 - 190,000; Full time

Job summary

Sherwin-Williams is seeking a Lead Machine Learning Engineer to design, develop, test, and operationalize ML and data solutions across cloud-based environments. You will guide model training, validation, and optimization, and ensure scalable, well-documented code with strong SDLC practices.

You will lead MLOps implementations, automate testing, and monitor deployments while collaborating with data scientists, software and platform engineers, QA, and PMs.

Qualifications

• Bachelor's Degree in Computer Science or Computer Engineering or equivalent with 9+ years of software development experience.
• 6+ years of experience in ML, MLOps, and/or software development.
• Proficient in SDLC, MLOps, and DevOps practices and tools.
• Experience with cloud platforms and software deployment for cloud-based tools.
• Strong SQL and relational database knowledge.
• Excellent written and verbal communication skills.
• Experience building microservices and integration patterns.
• Familiarity with Generative AI tools in SDLC (e.g., Claude Code, Codex, GitHub Copilot).
• Experience with Generative AI systems (LLMs, RAG, agent workflows).
• Knowledge of data science/statistics relevant to ML.

Responsibilities

• Design, develop, test, and operationalize ML and data solutions.
• Oversee design, development, and deployment of ML models per requirements.
• Guide training, validation, and optimization of models for performance.
• Provide strategic direction for maintainable, scalable code.
• Maintain technical documentation including deployment workflows and procedures.
• Lead MLOps efforts: CI/CD pipelines, testing, and monitoring.
• Foster cross-functional collaboration to translate business needs into production solutions.
• Provide ongoing support including incident management and observability.
• Stay updated on ML and MLOps advancements and introduce innovations.

Skills

ML/MLOps

SDLC methodologies

Cloud platforms

Programming: Python/R

Communication

Education

Bachelor's Degree in CS/CE

Tools

Docker

Kubernetes

Jenkins

MLflow

GitHub

Job description

The Lead 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.

Oversee the design, development, and deployment of machine learning models and other data solutions in alignment with business requirements and technical specifications defined

Guide the training, validation, and optimize machine learning models and other data solutions to ensure high performance and accuracy. Implement techniques for model/solution improvement and fine-tuning.

Provide strategic direction in the writing of high-quality, maintainable, and scalable code

Expert in Creating and maintaining technical documentation including model development processes, deployment workflows, operational procedures, code comments, design documents, and user manuals

MLOps Implementation: Provide expert guidance through the development and implementation of CI/CD pipelines for seamless model deployment. Guide 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

Foster cross functional collaboration 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.

This position can be remote.

This position is not eligible for sponsorship for work authorization now or in the future, including conversion to H1-B visa. Must be legally authorized to work in the country of employment without needing sponsorship for employment work visa status now or in the future.

Job duties include contact with other employees and access confidential and proprietary information and/or other items of value, and such access may be supervised or unsupervised. The Company therefore has determined that a review of criminal history is necessary to protect the business and its operations and reputation and is necessary to protect the safety of the Company's staff, employees, and business relationships.

Education and Experience

Required

• Bachelor's Degree in Computer Science or Computer Engineering or in lieu of a degree, at least 9 years of experience in software development
• 6+ years of experience in ML, MLOps, and/or software developmen
• Highly proficient knowledge of software development life cycle (SDLC) methodologies and practices, particularly MLOps and DevOps
• Highly proficient knowledge of software development tools, platforms and languages, particularly cloud platforms
• Proven experience in developing, integrating, testing and deploying software solutions for cloud-based or internet-related tools
• Expert of relational databases and SQL
• Excellent written and verbal communication skills
• Proven experience in developing Microservices and in-depth knowledge of various Integration patterns
• Use of Generative AI tools for SDLC, such as Claude Code, Codex, or Github Copilot
• Experience developing Generative AI systems, such as LLM-based chatbots, RAG systems, agentic workflows, or the like.
• Database Management Systems
• Data Science or Statistics experience, particularly as relates to Machine Learning
• Programming Languages, particularly Python or R
• Cloud Technologies & Platforms
• Software Development Lifecyle (SDLC) Methodology
• Continuous Integration and Continuous Deployment (CI/CD)
• Web Services & APIs
• Familiarity with MLOps practices, including CI/CD pipelines, automated testing, and model monitoring.
• Experience with tools such as Docker, Kubernetes, Jenkins, and MLflow
• Containerization and cloud-native development
• Source Control Systems, particularly github
• Build Tools and Compilers
• Software Testing & Quality Assurance Methodologies & Tools
• Automated Testing Tools
• Solution Architecture Patterns
• Data Science platforms
• Systems Integ