Senior Software Engineer, Factory Automation AI

About the Role

Key Responsibilities

• Work directly with the automation engineering team to develop and implement strategies for seamless deployment of machine learning models into our automation products.
• Complete the operational aspects of AI and machine learning projects, ensuring robust and scalable deployment.
• Build and maintain the infrastructure required for efficient data processing, model training, and inference at scale.
• Set up and manage monitoring systems to ensure the performance, reliability, and scalability of AI/ML models in production.
• Automate the end-to-end machine learning pipeline, including data collection, preprocessing, model training, deployment, and monitoring.
• Work closely with data scientists, data engineers, and software engineers to streamline the workflow from development to production.
• Ensure that AI/ML systems comply with security standards and best practices, addressing data privacy and protection concerns.
• Conduct performance tuning, troubleshooting, and optimization of AI and ML models to improve efficiency and reduce latency.
• Evaluate, select, and integrate appropriate tools and frameworks for model training, deployment, and monitoring (e.g., MLflow, Kubeflow, TensorFlow Extended).
• Maintain comprehensive documentation of processes, configurations, and performance metrics of deployed AI/ML models.
• Foster a culture of continual improvement by staying updated with the latest advancements in AIML Ops and implementing best practices.
• Develop and implement incident response plans to address and mitigate issues with deployed models promptly.
• Plan and implement strategies for scaling AI/ML models to handle increased demand and larger datasets.
• Proactively propose new solutions and enhancements leveraging AI/ML to address business challenges and improve operational efficiency.
• Work with cross-functional teams to incorporate AI/ML insights into broader business strategies and processes.

Requirements

• Bachelor's or higher in Computer Science, Software Engineering, or a related field.
• 5+ years of professional software development experience, with a significant focus on AI/ML ops.
• Extensive experience with AI and machine learning tools and frameworks, such as TensorFlow, PyTorch, or similar.
• Familiarity with manufacturing automation, including robotics, PLC's, automated NDT, and machine vision.
• Experience with AI edge deployments and integration into OT devices.
• Advanced understanding of microservices architecture and best practices.
• Experience with serverless computing frameworks and technologies.
• Familiarity with software development in agile and scrum environments.
• Experience with database management systems, both SQL and NoSQL (e.g., PostgreSQL, MongoDB).
• Advanced knowledge of cybersecurity best practices and compliance issues.
• Strong project management skills with the ability to manage multiple priorities.
• Certifications such as AWS Certified DevOps Engineer, Google Cloud Professional DevOps Engineer, Certified Kubernetes Administrator (CKA), or similar.
• Strong analytical and problem-solving skills with attention to detail.
• Excellent written and verbal communication skills for clear documentation and cross-team collaboration.
• Ability to innovate and adapt to rapidly changing technologies and environment.
• Ability to earn trust, maintain positive and professional relationships, and contribute to a culture of inclusion.
• Must be a U.S. citizen or national, U.S. permanent resident (Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.

Nice to Have

• Experience with AI and machine learning tools and frameworks, such as TensorFlow, PyTorch, or similar.
• Familiarity with manufacturing automation, including robotics, PLC's, automated NDT, and machine vision.
• Experience with AI edge deployments and integration into OT devices.
• Advanced understanding of microservices architecture and best practices.
• Experience with serverless computing frameworks and technologies.
• Certifications such as AWS Certified DevOps Engineer, Google Cloud Professional DevOps Engineer, Certified Kubernetes Administrator (CKA), or similar.

Benefits & Perks

• Medical, dental, vision, basic and supplemental life insurance, paid parental leave, short and long-term disability, 401(k) with a company match of up to 5%, and an Education Support Program.
• Paid Time Off: Up to four weeks per year and up to 14 company-paid holidays.
• Discretionary bonus based on individual contributions and company results.
• Eligibility for benefits varies by role; check with your recruiter for details.