Key Responsibilities:

• Design, implement, and maintain robust CI/CD pipelines for ML and software projects
• Support the full SDLC (Software Development Life Cycle), ensuring smooth integration, testing, deployment, and monitoring
• Build and manage ML model deployment pipelines, including containerization, versioning, rollback, and orchestration
• Automate testing, quality assurance, and performance checks for Python-based machine learning code
• Develop and maintain infrastructure-as-code solutions for repeatable and consistent environments
• Implement observability best practices, including monitoring, alerting, logging, and metrics
• Handle secrets management and enforce security practices in all DevOps processes
• Collaborate with cross-functional teams to translate business requirements into operational systems
• Identify and troubleshoot infrastructure and deployment issues, providing scalable solutions
• Document architectures, processes, and configurations clearly and concisely

Qualifications:

Must-Have Technical Skills:

• Strong experience with general DevOps tooling and practices
• Proficient in Python, with experience in testing frameworks (e.g., pytest)
• Deep knowledge of CI/CD tools (e.g., GitHub Actions, Jenkins, GitLab CI, etc.)
• Familiarity with SDLC processes, change control, and release management
• Hands-on experience with ML pipeline orchestration tools (e.g., MLflow, Airflow, Kubeflow)
• Experience with Lightspeed for scalable ML workflows
• Proficient with Helm for Kubernetes application packaging and deployment
• Hands-on experience with monitoring and logging tools (e.g., Prometheus, Grafana, ELK)
• Solid understanding of secrets management (e.g., HashiCorp Vault, AWS Secrets Manager, CyberArk)
• Strong SQL skills for data validation and diagnostics
• Proficient with Git, shell scripting, and Linux environments
• Familiarity with containerization and orchestration (e.g., Docker, Kubernetes)

Soft Skills & Business Acumen:

• Strong problem-solving and debugging skills
• High adaptability and comfort working in ambiguity
• Ability to translate loosely defined business needs into technical solutions
• Excellent communication skills for both technical and non-technical stakeholders
• A collaborative mindset and proactive attitude toward improvement

Nice to Have:

• Experience deploying ML models in production at scale
• Familiarity with cloud platforms (AWS, GCP, or Azure)
• Exposure to data governance, access control, and compliance in ML workflows
  • Understanding of feature stores and model registries