Sonata Software - Lead MLOps/DevOps Engineer

What We're Looking For :

This lead-level role owns the design and operation of MLOps and DevOps platforms that support reliable model build, test, deployment, and release workflows. The position combines cloud-native infrastructure, container orchestration, automation, and production model serving to improve delivery speed, governance, and operational stability across machine learning environments.

Key Responsibilities :

• Design and operate scalable MLOps pipelines that support reliable build, test, deployment, and release processes for machine learning workloads.
• Lead the implementation of cloud-native infrastructure, container orchestration, and automation practices across development and production environments to improve scalability and repeatability.
• Establish monitoring, logging, and alerting standards for model and platform services to improve reliability, performance, and incident response.
• Partner with data science, engineering, and security teams to productionize machine learning workloads with governance, repeatability, and compliance.
• Define deployment standards for model serving, artifact promotion, and environment parity to reduce release risk and accelerate production adoption.
• Drive technical best practices, mentor engineers on platform automation patterns, and continuously improve delivery speed and operational efficiency.
• Strengthen secrets handling, access controls, and release governance across CI/CD and runtime environments to improve security and audit readiness.

Must-Have Skills :

• MLOps &
• Model Lifecycle: MLOps, Model deployment and serving
• Cloud-Native Infrastructure &
• Orchestration: Containers, Kubernetes
• Delivery Automation &
• Release Engineering: CI/CD pipelines, Artifact and package management
• Infrastructure Provisioning &
• Security: Infrastructure as Code, Secrets management
• Monitoring and observability
• Python

Technical Skills :

• Operating Systems &
• Scripting: Linux, Bash and Python
• Version Control &
• Source Management: Gitlab, GitHub
• Cloud Platforms: AWS (Amazon Web Services)
• Containerization &
• Deployment: Docker, Kubernetes
• Infrastructure Automation &
• Configuration Management: Terraform, Ansible, CloudFormation
• CI/CD Platforms: GitLab CI/CD
• Observability Tools: DataDog or Dynatrace
• MLOps Platforms &
• Workflow Orchestration: MLflow, Airflow, AWS SageMaker
• Data &
• API Integration: SQL, REST APIs
• Cloud Security &
• Access Control: IAM and cloud security controls

Why Join This Opportunity?

• Own the platform layer that turns machine learning models into reliable production services.
• Influence DevOps and MLOps standards across build, deployment, observability, and governance workflows.
• Work at lead level on cloud-native automation and model-serving patterns that improve release speed and operational stability.