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.