About the Role:

We're looking for an experienced MLOps Engineer to drive ML automation, deployment and platform reliability.

Key Technical Skills:

DevOps &

- Cloud Infrastructure:
- Python, Bash / Go
- Docker and Kubernetes
- AWS / Azure / GCP
- Terraform, Pulumi or CloudFormation

CI/CD &

- ML Pipelines:
- GitHub Actions, GitLab CI, Jenkins or Argo Workflows
- Kubeflow, Airflow, Prefect or Metaflow
- Automated model testing, versioning and deployment
- Canary deployments and ML workflow automation

Model Deployment &

- Serving:
- TorchServe, NVIDIA Triton, TensorFlow Serving
- Seldon, BentoML or similar
- Batch and real-time model serving

Monitoring &

- MLOps:
- Prometheus, Grafana, Datadog
- MLflow, Weights &
- Biases or Neptune
- Model monitoring, data drift and performance tracking
- Feature stores - Feast / Tecton
- Data versioning - DVC / LakeFS

Key Responsibilities:

- Build and manage end-to-end ML pipelines.
- Automate model training, validation and deployment.




- Develop robust CI/CD pipelines for ML workflows.
- Manage scalable Kubernetes-based ML infrastructure and GPU clusters.
- Implement model monitoring, observability and alerting.
- Maintain experiment tracking, model registries and data lineage.
- Optimize cloud infrastructure performance and costs.
- Collaborate with Data Science teams to accelerate production AI/ML deployments.

Good to Have:

- Experience with GPU infrastructure and distributed ML workloads.
- Exposure to LLM deployment, Vector Databases and RAG infrastructure.
- Experience with feature stores and data governance.

Ideal Candidate:

- A hands-on MLOps / DevOps professional with solid cloud, Kubernetes, CI/CD and ML infrastructure expertise, capable of building reliable and scalable production ML platforms.

📌 Senior MLOps/DevOps Engineer (India)
🏢 Twinpacs Sdn Bhd
📍 India