Principal Machine Learning Engineer

What you'll do

• Lead end-to-end solution delivery and develop scalable cloud security architecture through strategic planning and coding.
• Architect the ML lifecycle, including model development, training, production deployment, and real-time inference; establish practices for versioning, reproducibility, auditing, and compliance.
• Build and optimize resilient CI/CD, monitoring, MLOps, and ML infrastructure systems for reliability, performance, and cost efficiency.

What they ask for

• 10+ years of software development experience focused on cloud-native and SaaS applications.
• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
• Experience designing large-scale distributed systems on public cloud platforms and proficiency in at least one of Python, Go, or Java.
• Strong machine learning and ML frameworks background; experience with the full ML lifecycle, model deployment, and MLOps; experience with IaC tools.

Nice to have

• Master's or PhD in Computer Science or a related technical field; 3+ years experience with data scientist/analyst or similar role.
• Cybersecurity or network security experience; Docker and Kubernetes; Kafka or Flink; open-source contributions in cloud-native or MLOps.

Pay

$163,200.00 - $264,000.00/yr (USD, base salary)

Tools

TensorFlow, PyTorch, Terraform, CloudFormation, AWS, GCP, Azure, Python

AI focus

Develop, train, deploy, and operate machine learning models, including real-time inference, MLOps, model versioning, reproducibility, auditing, and monitoring.