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.