Senior DevOps/MLOps Engineer

citi

Chennai NM Years Exp Posted 332d ago

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

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

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