Job Description

Roles & Responsibilities

Responsibilities

• Design, build, and maintain scalable ML pipelines for training, testing, and deployment
• Deploy & maintain machine learning models and ensure their performance, reliability, and monitoring
• Collaborate with data scientists and engineers to streamline experimentation and deployment workflows
• Implement CI/CD practices for ML systems (ML CI/CD)
• Manage and optimize cloud-based infrastructure for ML workloads
• Develop monitoring, logging, and alerting systems for model performance and data drift
• Ensure reproducibility, versioning, and governance of ML models and datasets
• Advocate for best practices in MLOps, DevOps, and software engineering

• 5+ years of experience in software engineering, DevOps, or MLOps roles
• Strong programming skills in Python (and familiarity with Java/Go is a plus)
• Experience with ML frameworks such as TensorFlow, PyTorch, or similar
• Hands-on experience with containerization and orchestration tools (Docker, Kubernetes)
• Experience with cloud platforms (AWS, GCP, or Azure)
• Familiarity with CI/CD tools (e.g., GitHub Actions, Jenkins, GitLab CI)
• Strong understanding of data pipelines, distributed systems, and API development
• Experience with monitoring tools and logging frameworks

Desired Candidate Profile

5+ years of experience in software engineering, DevOps, or MLOps roles

Strong programming skills in Python (and familiarity with Java/Go is a plus)

Experience with ML frameworks such as TensorFlow, PyTorch, or similar

Hands-on experience with containerization and orchestration tools (Docker, Kubernetes)

Experience with cloud platforms (AWS, GCP, or Azure)

Familiarity with CI/CD tools (e.g., GitHub Actions, Jenkins, GitLab CI)

Strong understanding of data pipelines, distributed systems, and API development

Experience with monitoring tools and logging frameworks