About the role
Title: MLOPS Engineer
Location: Concord, CA(Onsite)
Job Type: Contract
Required Technical Skills
• Strong hands-on experience with MLOps and Machine Learning lifecycle management.
• Strong programming experience in Python and scripting for automation.
• Experience building and maintaining end-to-end ML pipelines for model training, validation, deployment, monitoring, and retraining.
• Hands-on experience with ML model deployment and productionization.
• Experience with MLflow, Kubeflow, SageMaker, Vertex AI, Azure Machine Learning, or similar ML platforms.
• Experience with model versioning, experiment tracking, model registry, and artifact management.
• Strong experience with Docker and containerized applications.
• Hands-on experience with Kubernetes for deploying and managing ML workloads.
• Experience with Amazon EKS, Google GKE, or Azure AKS is preferred.
• Strong understanding of CI/CD pipelines and automated ML deployment workflows.
• Experience with Jenkins, GitHub Actions, GitLab CI/CD, Azure DevOps, or Cloud Build.
• Experience with Git and version-control workflows.
• Hands-on experience with Terraform or other Infrastructure as Code (IaC) tools.
• Experience deploying and managing ML workloads on AWS, GCP, or Azure.
• Experience implementing real-time and batch model inference/serving.
• Experience with ML model monitoring, including model drift, data drift, model performance, latency, and prediction quality.
• Experience with monitoring and observability tools such as Prometheus, Grafana, CloudWatch, Azure Monitor, or Google Cloud Monitoring.
• Knowledge of ML model governance, reproducibility, lineage, security, and access control.
• Experience with KServe, Seldon, NVIDIA Triton, TensorFlow Serving, or TorchServe is a plus.
• Experience with Airflow, Argo Workflows, Kubeflow Pipelines, or similar workflow orchestration tools is preferred.
• Experience with LLM/Generative AI MLOps, RAG pipelines, or AI/ML platforms is a plus.
• Knowledge of feature stores, vector databases, model serving, and ML infrastructure optimization is preferred.
• Strong understanding of cloud security, secrets management, IAM, networking, and secure deployment practices.
• Experience troubleshooting and optimizing production ML systems for scalability, reliability, performance, and cost.
Thanks
Aatmesh
aatmesh.singh@ampstek.com
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