MLOps Engineer

Noda

full-time

Posted on: 9/29/2025

Location Type: Hybrid

Location: Austin • Texas • 🇺🇸 United States

✨ AI Apply

Mid-LevelSenior

AirflowApacheAWSAzureCloudDockerKubernetesPythonPyTorchTensorflow

About the role

Own the complete lifecycle of machine learning models for autonomous vehicle orchestration
Build infrastructure, pipelines, and monitoring systems to deploy, validate, and continuously improve AI agents and reasoning systems
Design and implement automated training pipelines for LLMs, agent frameworks, and reasoning models
Build model versioning, experiment tracking, and artifact management systems
Develop automated model validation and testing frameworks, including simulation-based evaluation of agent behaviors
Implement A/B testing infrastructure for comparing AI reasoning strategies
Create model monitoring and observability systems to track performance, drift, and reliability
Optimize model deployment for edge computing (quantization, pruning, inference acceleration)
Build automated retraining pipelines using operational feedback from field deployments
Implement model governance and compliance frameworks, including audit trails and safety validation
Design feature stores and data pipelines preparing operational sensor and mission data
Support rollback and canary deployment strategies for safe model updates
Ensure secure model deployment practices, including model encryption, access controls, and adversarial robustness validation

4+ years of experience in MLOps, ML platform engineering, or production machine learning systems
Strong proficiency in Python
Experience with ML frameworks (PyTorch, TensorFlow, Hugging Face, MLflow, or similar)
Experience with container orchestration for ML workloads (Docker, Kubernetes)
Knowledge of model serving frameworks (TorchServe, TensorFlow Serving, Triton, or cloud ML endpoints)
Understanding of ML experiment tracking, model versioning, and artifact management systems
Experience with cloud ML platforms (AWS SageMaker, Azure ML, or Google AI Platform)
Proficiency with data pipeline tools (Apache Airflow, Prefect, or similar)
Knowledge of model monitoring, performance tracking, and automated alerting systems
Understanding of CI/CD practices specifically applied to ML model deployment
U.S. Citizenship with ability to obtain a security clearance

Benefits

Tip: use these terms in your resume and cover letter to boost ATS matches.

MLOpsmachine learningPythonPyTorchTensorFlowHugging FaceMLflowDockerKubernetesCI/CD