At Dynamo AI, a Senior Software Engineer in the Core Infra team will design, develop, and maintain robust, secure, and scalable infrastructure and applications. You will be instrumental in deploying our advanced machine learning models in diverse production environments, ensuring optimal performance and reliability. You build full set of features in our Kubernetes based infrastructure and apply the best practices in the industry.

Responsibilities

Design, develop, and maintain our Kubernetes-based infrastructure, enabling seamless deployment and autonomous management of ML models across various customer production environments
Take full ownership of the infrastructure, proactively identifying and resolving issues to maintain the highest levels of quality, performance, and reliability in customer deployments
Be innovative, apply industry best practices in both software development and infrastructure management to ensure robust, scalable systems
Develop and maintain automated testing frameworks and CI/CD pipelines to optimize code quality, deployment speed, and operational efficiency

Qualifications

3+ years of experience developing and managing scalable services in Kubernetes
Proficiency in Kubernetes, cloud services, and modern DevOps practices
Proven track record of collaborating with customers to resolve deployment challenges in production environments
Strong programming skills in Python or other relevant languages
Excellent problem-solving abilities and a commitment to innovation and continuous improvement
Experience in deploying machine learning models in production is a plus

Dynamo AI is committed to maintaining compliance with all applicable local and state laws regarding job listings and salary transparency. This includes adhering to specific regulations that mandate the disclosure of salary ranges in job postings or upon request during the hiring process. We strive to ensure our practices promote fairness, equity, and transparency for all candidates.

Salary for this position may vary based on several factors, including the candidate's experience, expertise, and the geographic location of the role. Compensation is determined to ensure competitiveness and equity, reflecting the cost of living in different regions and the specific skills and qualifications of the candidate.