Senior Software Engineer, ML Ops
Designs and scales reliable machine learning infrastructure, automation, and deployment workflows across AWS and on-premises environments. Requires 5+ years of software engineering experience, strong Python and backend skills, Kubernetes, cloud, observability, and infrastructure-as-code expertise.
About the job
Responsibilities
- Architect and build machine learning infrastructure and automation in AWS and on-premises environments.
- Lead system design and architectural discussions for MLOps platforms, addressing performance, security, and compliance requirements.
- Research, evaluate, and implement MLOps tools, frameworks, and best practices.
- Collaborate with machine learning engineers, data scientists, product engineering, and infrastructure teams to move research into production.
- Optimize machine learning workflows for efficient and reproducible model deployment and monitoring.
- Establish engineering standards, conduct design reviews, and mentor junior engineers.
- Automate machine learning operations, including model CI/CD, feature engineering pipelines, and deployment strategies using Kubernetes, Airflow, and other orchestration tools.
Requirements
- Bachelor's or master's degree in computer science, computer engineering, software engineering, or a closely related field.
- 5+ years of software engineering experience focused on production-grade frameworks or applications.
- Strong software engineering skills in complex, multi-language systems and scalable backend architecture.
- Experience with Kubernetes and cloud computing platforms, preferably AWS.
- Experience with observability and monitoring tools such as Prometheus, Grafana, or Datadog.
- Understanding of DevOps principles and infrastructure as code, including Helm and Terraform.
- Experience owning development platforms and serving internal customers.
- Proficiency in Python and exposure to additional programming languages.
Preferred Qualifications
- Experience with machine learning frameworks such as PyTorch or scikit-learn.
- Experience with data workflow orchestration frameworks such as Airflow or Kubeflow.
- Expertise in MLOps principles, including model lifecycle management, feature stores, model monitoring, and machine learning CI/CD.
- Experience with streaming data processing using Kafka, Flink, or Spark Streaming.
- Familiarity with security and compliance best practices for machine learning systems.
- Experience using AI development assistants such as Copilot or Cursor.
Compensation
- Expected annual salary range: $127,500–$195,500.
Skills
Python, Kubernetes, AWS, Prometheus, Grafana, Datadog, Helm, Terraform, PyTorch, scikit-learn, Airflow, Kubeflow, Kafka, Flink, Spark
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