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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior ML Ops Engineer (Machine Learning Infrastructure) - **Company:** Parallel Systems - **Location:** Los Angeles, CA, United States - **Experience:** Expert - **Salary:** $150,000.0 - $250,000.0 - **Contract:** Permanent contract - **Skills:** Airflow, Amazon Web Services, Computer Vision, Microsoft Azure, Cloud Computing, Continuous Integration, Python (Programming Language), Machine Learning, Cloud Services, Data Ingestion, Pytorch, Delivery Pipeline, Deep Learning, Model Validation, Git, Kubernetes, Information Technology, Data Management, Machine Learning Operations - **Published:** July 29, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=0b55e4de52b04548 ## About the Role This can be a hybrid role (minimum of 1 week per month onsite in Los Angeles) for a senior engineer with experience in 0 to 1 builds of perception systems., * Bachelor's or higher degree in Computer Science, Machine Learning, or a relevant engineering discipline. * 5+ years of experience building large-scale, reliable systems; 2+ years focused on ML infrastructure or MLOps. * Proven experience architecting and deploying production-grade ML pipelines and platforms. * Strong knowledge of ML lifecycle: data ingestion, model training, evaluation, packaging, and deployment. * Hands-on experience with MLOps tools (e.g., MLflow, Kubeflow, SageMaker, Airflow, Metaflow, or similar). * Deep understanding of CI/CD practices applied to ML workflows. * Proficiency in Python, Git, and system design with solid software engineering fundamentals. * Experience with cloud platforms (AWS, GCP, or Azure) and designing ML architectures in those environments., * Experience with deep learning architectures (CNNs, RNNs, Transformers) or computer vision. * Hands-on experience with distributed training tools (e.g., PyTorch DDP, Horovod, Ray). * Background in real-time ML systems and batch inference, including CPU/GPU-aware orchestration. * Previous work in autonomous vehicles, robotics, or other real-time ML-driven systems. ## Description * Design and implement robust MLOps solutions, including automated pipelines for data management, model training, deployment and monitoring. * Architect, deploy, and manage scalable ML infrastructure for distributed training and inference. * Collaborate with ML engineers to gather requirements and develop strategies for data management, model development and deployment. * Build and operate cloud-based systems (e.g., AWS, GCP) optimized for ML workloads in R&D, and production environments. * Build scalable ML infrastructure to support continuous integration/deployment, experiment management, and governance of models and datasets. * Support the automation of model evaluation, selection, and deployment workflows. What Success Looks Like: * After 30 Days: You have developed a deep understanding of the product goals, existing infrastructure, and stakeholder requirements. You've conducted technical discovery and proposed a preliminary MLOps architecture-evaluating various ML tools, cloud services, and workflow strategies-clearly outlining pros and cons for each option. * After 60 Days: You've delivered a detailed design document that outlines the end-to-end ML pipeline, including data ingestion, model training, deployment, and monitoring. Based on feedback from ML engineers and stakeholders, you've iterated on the design and built PoC for the core ML workflow aligned with the approved architecture. * After 90 Days: You have delivered the core features of the MLOps pipeline and successfully integrated key tools (e.g., MLflow, SageMaker, or Kubeflow). You've also initiated the implementation of the remaining features, ensuring the infrastructure supports scalable, repeatable workflows for model experimentation and deployment in both R&D and production environments. ## Related Videos - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)