MLOps Engineer

What you will do

Develop and manage scalable, automated machine learning pipelines, CI/CD workflows, and orchestration frameworks
Design and implement robust model serving infrastructure using platforms like TorchServe, TensorFlow, Triton etc.
Develop scalable inference architectures optimized, with ultra-low latency and high throughput
Ensure seamless model deployment by implementing A/B testing, canary releases, and rollback capabilities
Develop logging, alerting, and monitoring solutions to track model development, and reliability
Improve GPU usage, enable autoscaling, and streamline resource allocation to boost efficiency
Design, implement, and maintain feature stores, robust data pipelines, and scalable storage solutions to efficiently handle large volumes of data

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent practical experience)
5+ years of experience as MLOps engineer or DevOps roles, working with MLOps platforms (MLflow, WandB etc..) and frameworks (PyTorch, TensorFlow etc..)
Experience building and designing MLOps infrastructure from the ground up
Experience with model serving frameworks (TorchServe, TensorFlow Serving, Triton, KServe etc..) for high scalability and low latency inference
Experience in building and managing data pipelines to support both model training and inference
Experience with Kubernetes on a major cloud provider (AWS, GCP, or Azure) and with infrastructure as code (e.g. Terraform, Helm, GitOps)
Strong software engineering skills in Python, Bash, and Go, with a focus on writing clean, maintainable, and scalable code
Experience in AI/ML systems security, compliance, and model governance
Proficient with observability and monitoring tools, such as Prometheus, Grafana, Datadog, and OpenTelemetry

Competitive compensation with salary and equity
Comprehensive health coverage for you and your dependents
Paid parental leave for all new parents, inclusive of adoptive and surrogate journeys

What you will do

Develop and manage scalable, automated machine learning pipelines, CI/CD workflows, and orchestration frameworks
Design and implement robust model serving infrastructure using platforms like TorchServe, TensorFlow, Triton etc.
Develop scalable inference architectures optimized, with ultra-low latency and high throughput
Ensure seamless model deployment by implementing A/B testing, canary releases, and rollback capabilities
Develop logging, alerting, and monitoring solutions to track model development, and reliability
Improve GPU usage, enable autoscaling, and streamline resource allocation to boost efficiency
Design, implement, and maintain feature stores, robust data pipelines, and scalable storage solutions to efficiently handle large volumes of data

What you need

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent practical experience)
5+ years of experience as MLOps engineer or DevOps roles, working with MLOps platforms (MLflow, WandB etc..) and frameworks (PyTorch, TensorFlow etc..)
Experience building and designing MLOps infrastructure from the ground up
Experience with model serving frameworks (TorchServe, TensorFlow Serving, Triton, KServe etc..) for high scalability and low latency inference
Experience in building and managing data pipelines to support both model training and inference
Experience with Kubernetes on a major cloud provider (AWS, GCP, or Azure) and with infrastructure as code (e.g. Terraform, Helm, GitOps)
Strong software engineering skills in Python, Bash, and Go, with a focus on writing clean, maintainable, and scalable code
Experience in AI/ML systems security, compliance, and model governance
Proficient with observability and monitoring tools, such as Prometheus, Grafana, Datadog, and OpenTelemetry