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- PythonTechnical 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 controlsWhy 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. (ref:hirist.tech)