Senior Software Engineer, PathAI

• You’re someone who enjoys designing and building for reliability, relishes collaboration and technical challenges, and takes pride in making things better – without taking yourself too seriously. Our technical space is broad: cloud infrastructure, Kubernetes, high-scale workloads, observability, distributed systems, and a bit of everything in between
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent experience)
• 5+ years of software engineering experience, with a focus on building production-grade frameworks or applications
• Strong software engineering skills in complex, multi-language systems and experience with scalable backend architecture
• Experience with Kubernetes and cloud computing platforms (AWS preferred)
• Experience with observability and monitoring tools (e.g., Prometheus, Grafana, Datadog)
• Understanding of DevOps principles and infrastructure-as-code (Helm, Terraform)
• Experience owning development platforms and serving internal customers
• Proficiency in Python + exposure to additional languages
• At PathAI, we are looking for individuals who are team players, are willing to do the work no matter how big or small it may be, and who are passionate about everything they do
• If this sounds like you, even if you may not match the job description to a tee, we encourage you to apply. You could be exactly what we're looking for

Desirable

• As a Senior Software Engineer, MLOps, you will play a key role in designing, developing, and scaling machine learning infrastructure that powers our enterprise AI systems
• Architect and build infrastructure and automation, in AWS and on-premises, to support ML application development and deployment
• Drive system design and lead architectural discussions for our MLOps suite, ensuring it meets performance, security, and compliance requirements
• Lead technical initiatives by researching, evaluating, and implementing new MLOps tools, frameworks, and best practices
• Collaborate with machine learning engineers, data scientists, product engineering, and infrastructure teams to bridge the gap between research and production
• Optimize ML workflows, ensuring models are efficiently and reproducibly deployed & monitored
• Champion engineering excellence by enforcing high coding standards, conducting design reviews, and mentoring junior engineers
• Automate ML operations, including CI/CD for ML models, feature engineering pipelines, and deployment strategies using Kubernetes, Airflow, and other orchestration tools