1 month ago
USA (city not specified)Mid Level
Base Salary
$103k - $155k/yr
Responsibilities
- Design and implement scalable, reliable, secure ML/AI platforms and standardized GenAI workflows.
- Partner with data scientists, product managers, security teams, and data engineers to deliver machine learning solutions.
- Implement CI/CD pipelines for machine learning models and data workflows using containerization, infrastructure-as-code, and orchestration technologies.
- Build automated model training, deployment, and lifecycle management processes.
- Prototype and evaluate MLOps technologies to improve efficiency, reduce costs, and enable product capabilities.
- Deploy, monitor, tune, and troubleshoot production machine learning models.
- Establish and track health, performance, reliability, and cost optimization metrics for AI systems.
- Participate in code and design reviews and contribute directly to implementation.
- Mentor junior engineers and share engineering and AI best practices.
Requirements
- At least 3 years of professional software engineering experience, including at least 1 year in ML/AI or big data environments.
- Proficiency in Python, Java, or C#.
- Production experience with Docker.
- Experience deploying cloud-based solutions on AWS, Azure, or GCP.
- Experience with infrastructure-as-code tools such as Terraform or Pulumi.
- Familiarity with workflow orchestration platforms such as Prefect or Airflow.
- Understanding of Kubernetes and Helm fundamentals.
- Experience deploying, monitoring, and troubleshooting machine learning models in production.
- Ability to collect and analyze model reliability and algorithm health metrics.
- Strong collaboration and communication skills with cross-functional stakeholders.
- A bachelor's degree in Computer Science, Engineering, Mathematics, or a related field is preferred.
- A master's degree in a relevant discipline is preferred.
- Experience with MLflow or Kubeflow, model optimization techniques, Spark, EMR, Kafka, TensorFlow, or PyTorch is preferred.
- Experience working in secure and compliant data processing environments is preferred.
Benefits
- Competitive base salary, annual performance bonus, long-term incentives, benefits, DTO, parental leave, and an equity program.
- Hybrid/remote work arrangement.
- Opportunities to work on distributed cloud-native systems and own systems end to end.
- Collaborative, inclusive environment focused on knowledge sharing and continuous improvement.
Tech Stack
Apache AirflowApache KafkaApache SparkAWSAzureC#DockerGoogle Cloud PlatformHelmJavaKubernetesMLflowPythonPyTorchTensorFlowTerraform