Job Description
Position: MLOps Engineer
Company: WillWare Technologies
Location: Bangalore
Work Mode: WFO
Required Qualifications:
• Orchestration: Deep experience with Valohai (Preferred), Kubeflow, Airflow, or AWS SageMaker Pipelines.
• Model Lifecycle: Expert-level knowledge of MLflow for tracking experiments and managing model registries.
• Cloud Proficiency: Hands-on experience with both Azure and AWS ecosystems.
• Coding: Strong proficiency in Python and shell scripting.
• Containers: Docker and container orchestration.
Key Responsibilities:
MLOps as Code & Orchestration
Company: WillWare Technologies
Location: Bangalore
Work Mode: WFO
Required Qualifications:
• Orchestration: Deep experience with Valohai (Preferred), Kubeflow, Airflow, or AWS SageMaker Pipelines.
• Model Lifecycle: Expert-level knowledge of MLflow for tracking experiments and managing model registries.
• Cloud Proficiency: Hands-on experience with both Azure and AWS ecosystems.
• Coding: Strong proficiency in Python and shell scripting.
• Containers: Docker and container orchestration.
Key Responsibilities:
MLOps as Code & Orchestration
- Design and implement MLOps as Code methodologies. pipelines, infrastructure, and configurations must be versioned, reproducible, and automated (GitOps).
- Manage and optimize deep learning orchestration platforms (specifically Valohai, or similar tools like Kubeflow/SageMaker Pipelines) to automate training, fine-tuning, and deployment workflows.
- Standardize execution environments using Docker and ensure reproducibility across local, dev, and production environments.
- Central Registry & Governance
- Own the Central Model Registry strategy using MLflow. Ensure strict versioning, lineage tracking, and stage transitions (Staging to Prod) for all models.
- Enforce governance policies for model artifacts, ensuring security and compliance across the model lifecycle.
- Multi-Cloud Architecture (Azure & AWS)
- Operate in a hybrid cloud environment. You will leverage Azure (AI Foundry, OpenAI Service) and AWS (SageMaker, Bedrock, EC2/GPU instances) based on workload requirements.
- Design seamless integrations between cloud storage (S3/Blob), compute, and the orchestration layer.
- Experience creating custom execution environments for specialized hardware (NVIDIA GPUs, TPUs).
- CI/CD & Automation
- Build robust CI/CD pipelines (GitHub Actions/Azure DevOps) that trigger automatic training or deployment based on code or data changes.
- Automate the 'hand-off' process between Data Scientists and production environments.
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