Sr. Machine Learning Ops Engineer

About Us At Hayden AI, we are on a mission to harness the power of computer vision to transform the way transit systems and other government agencies address real-world challenges. From bus lane and bus stop enforcement to transportation optimization technologies and beyond, our innovative mobile perception system empowers our clients to accelerate transit, enhance street safety, and drive toward a sustainable future. About the role: As a Senior MLOps Engineer within the Perception Deep Learning team, you will lead the design and evolution of our machine learning platform, enabling teams to build, deploy, and scale intelligent systems with reliability and speed. In this role, you will partner closely with perception, deep learning and platform engineers to build infrastructure to train and operationalize machine learning models and drive best practices across the ML lifecycle. You will play a key role in shaping the architecture of our ML infrastructure, from data ingestion and training pipelines to deployment, monitoring, and governance. As a senior member of the team, you will influence technical strategy, mentor engineers, and champion a culture of reproducibility, observability, and continuous improvement. Key responsibilities: Architect, design, deploy, and operate scalable cloud-based MLOps platforms and workflows that enable efficient training, evaluation, deployment, monitoring, and lifecycle management of AI/ML models. Own the technical strategy and evolution of ML infrastructure, identifying architectural bottlenecks and driving cross-functional initiatives to improve developer productivity, experimentation velocity, scalability, and operational efficiency. Build robust, reliable, and automated systems that enable teams to ship new models and features rapidly while maintaining high standards for quality, reproducibility, observability, security, and production reliability. Define and implement infrastructure optimization strategies that balance performance, scalability, reliability, and cost across cloud and compute resources. Evaluate emerging tools, technologies, and industry best practices in MLOps, cloud infrastructure, and ML systems, and lead their adoption where they can meaningfully improve ML development and production workflows. Establish engineering best practices for ML infrastructure, including system design, code quality, testing, CI/CD, monitoring, documentation, and operational readiness. Provide technical leadership and mentorship to engineers, lead design and code reviews, and help raise the engineering quality and technical capabilities of the broader team. Partner closely with ML engineers, researchers, data engineers, and product teams to translate evolving AI/ML requirements into scalable and maintainable infrastructure solutions. Drive complex, ambiguous infrastructure projects from technical strategy and architecture through implementation, production deployment, and long-term operational ownership. Key Qualifications: A Bachelors Degree or a Masters Degree in Computer Science, Electrical Engineering, or a related field. Core Skills: General Software Engineering skills with 6+ years of programming experience in python and the surrounding tooling ecosystem along with familiarity in linux and expertise in infrastructure, cloud and/or MLOps,. Personal Attributes: Team player, good communication skills, self starter. Strong teamwork and communication skills to collaborate with cross-functional teams, including ML and software engineers. Nice to Have: Experience building MLOps pipelines for deep learning based perception solutions on AWS or GCP