Software Engineer 4

• As a Senior Software Engineer, you will be responsible for developing and maintaining the infrastructure required to deploy, monitor, and manage machine learning models efficiently and effectively.
• This role is focused on building ML-Ops solutions, but general software engineering skills are sufficient. 
• The work is critical in bridging the gap between research and engineering, ensuring that our AI solutions are scalable, reliable, and seamlessly integrated into our products.
• This role requires you to thrive in a fast-paced environment, be passionate about AI/ML, and be constantly looking for ways to optimize and automate machine learning workflows.

 
Responsibilities

• Pipeline Development: Implement, optimize, and maintain CI/CD pipelines for ML systems, including integrations with GitHub workflows and Jenkins.
• Collaboration: Partner with data scientists, frontend engineers, and platform teams to deliver seamless integration of ML models into core evaluation platforms.
• Environment Management: Administer ML development/production environments using cloud-native solutions; optimize for scalability, reliability, and cost.
• Tooling and Automation: Evaluate, build, and deploy automation tools to streamline the end-to-end ML lifecycle.
• Quality & Monitoring: Enhance and develop quality evaluation features and ensure robust monitoring via dashboards and automated alerts.
• Documentation & Best Practices: Champion engineering best practices, promote code quality, and document workflows, tools, and processes for effective team adoption.

 
Qualifications:

• Python, Typescript, Shell script languages
• Experience with ML pipeline tools (Kubeflow, Airflow, MLflow)
• Services on AWS such as S3, Lambda, DynamoDB
• CI/CD systems (GitHub Actions, Jenkins, GitLab)
• Infrastructure-as-Code experience (Terraform, CloudFormation)
• Containerization (Docker, Kubernetes)
• Communication and documentation skills
• Strong problem-solving skills and the ability to work collaboratively across teams.
• Strong knowledge of ML-Ops a bonus
• CI/CD systems (GitHub Actions, Jenkins, GitLab)
• Infrastructure-as-Code experience (Terraform, CloudFormation)

 
Profile:

• Master's in computer science or related STEM field
• Minimum 5 years in software engineering; at least 2 years dedicated to DevOps/MLOps in cloud and production environments.
• Industry experiences building end-to-end software pipelines and infrastructure with deep experience with Kubernetes, Infrastructure as Code (Terraform, CloudFormation), AWS, and GCP.
• Expert proficiency in Python; working knowledge of ML frameworks (e.g., PyTorch, TensorFlow, MLflow)
• Practical experience with cloud and NoSQL databases such as DynamoDB; SQL databases a plus.
• Skilled with GitHub Actions, Jenkins, GitLab CI, Docker, and related automation platforms.
• Exposure to Computer Vision, Generative AI (GAN, CLIP, Diffusion, MLLM), and their practical deployment for evaluation systems.
• Experience in integrating ML workflows with user-facing features and backend pipelines.
• Strong problem-solving, excellent written/verbal communication, and the ability to lead and collaborate effectively across teams