Key Responsibilities:

• Design, implement, and maintain robust CI/CD pipelines for application and ML workloads.
• Build and manage cloud-native infrastructure using Infrastructure as Code (IaC) principles.
• Enable end-to-end MLOps lifecycle including model training, versioning, validation, deployment, monitoring, and retraining.
• Collaborate with application developers, data scientists, and platform teams to operationalize solutions efficiently.
• Ensure high availability, scalability, performance, and security of platforms and services.
• Implement monitoring, logging, alerting, and observability frameworks for proactive incident management.
• Automate environment provisioning, configuration management, and release processes.
• Enforce DevSecOps practices including vulnerability management, secrets management, and compliance controls.
• Troubleshoot complex infrastructure, deployment, and production issues.
• Evaluate emerging DevOps/MLOps tools and recommend adoption aligned to business needs.
• Mentor junior engineers and contribute to standards, best practices, and technical documentation

Syngene Values

All employees will consistently demonstrate alignment with our core values

•  Excellence
•  Integrity
•  Professionalism

Qualifications & Experience

Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related discipline.

Experience:

Expertise in DevOps, Platform Engineering, and/or MLOps roles.

Proven experience supporting enterprise-scale or cloud-native platforms.

Preferred Skills & Expertise:

Cloud & Infrastructure:

• Strong experience with AWS / Azure / GCP
• Infrastructure as Code using Terraform, ARM, CloudFormation, or similar tools

DevOps & Automation:

• CI/CD tools such as Azure DevOps, GitHub Actions, GitLab CI, Jenkins
• Configuration management and automation (Ansible, Bash, Python)
• Containerization using Docker and orchestration with Kubernetes

MLOps:

• Experience operationalizing ML models using tools such as MLflow, Kubeflow, SageMaker, Azure ML, or Vertex AI
• Model versioning, experiment tracking, feature stores, and model monitoring
• Understanding of data pipelines and ML lifecycle management

Monitoring & Security:

• Monitoring and observability tools (Prometheus, Grafana, ELK, Azure Monitor, CloudWatch)
• DevSecOps practices, IAM, secrets management, and compliance controls

Engineering Fundamentals:

• Solid understanding of system architecture, networking, and distributed systems
• Strong debugging, analytical, and problem-solving skills
  • Excellent written and verbal communication skills