> Markdown version of [/jobs/ext/3872511-senior-machine-learning-engineer-applied-intelligence-in-santa-ana](https://www.wearedevelopers.com/jobs/ext/3872511-senior-machine-learning-engineer-applied-intelligence-in-santa-ana). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Senior Machine Learning Engineer (Applied Intelligence) in Santa Ana

- **Company:** Energy Jobline
- **Location:** Santa Ana, CA, United States
- **Experience:** Expert
- **Salary:** $220,000.0 - $292,000.0
- **Contract:** Permanent contract
- **Skills:** Airflow, Computer Vision, Software Code Optimization, Information Engineering, Data Governance, Supervisory Control and Data Acquisition (SCADA), Python (Programming Language), Machine Learning, Inference Optimization, Tensorflow, Azure Machine Learning, Smart Devices, Software Engineering, Workflow Management Systems, Pinecone, Feature Store, Enterprise Software Applications, Real Time Systems, Pytorch, Retrieval-Augmented Generation, System Availability, Delivery Pipeline, Large Language Models, Deep Learning, Model Validation, Discretization, Event Driven Architecture, Kubernetes, Information Technology, Low Latency, Deployment Automation, Weaviate, Milvus, Triton Inference Server, Machine Learning Operations, TensorRT, Hardware Infrastructure, Software Version Control, Data Pipelines, Docker
- **Published:** October 22, 2026
- **Apply:** https://www.energyjobline.com/job/senior-machine-learning-engineer-applied-intelligence-santa-ana-32028507

## About the Role

* 6+ years of experience in software engineering or ML engineering roles building production systems, ideally in fast-paced environments
\n

 * Strong production MLOps experience: data pipeline management, model versioning, deployment automation, and production monitoring
\n

 * Proven track record of taking ML models from prototype to production and maintaining them long-term
\n

 * Experience with on-call rotations and production incident management for data or ML systems
\n

 * Strong proficiency in Python and experience with deep learning frameworks (PyTorch, TensorFlow)
\n

 * Experience building and deploying containerized ML services using Docker and Kubernetes
\n

 * Solid data engineering skills including pipeline orchestration, data quality management, and working with time-series or event-driven data
\n

 * Experience implementing observability for model performance, inference accuracy, and data drift
\n

 * Strong technical communication skills with ability to work across ML research, data engineering, and operations teams
\n

 * Experience building systems that must operate reliably under real-world operational constraints (high availability, low latency, constrained environments)
\n

 * Proven ability to coordinate across multiple teams and translate requirements into technical solutions
\n

 * Degree in Computer Science, Engineering, or related technical field, or equivalent practical experience
\n

 * U.S. Person status is required as this position needs to access export controlled data
\n, * Experience in manufacturing, industrial, or OT-adjacent domains (MES, SCADA, factory automation, IoT)
\n

 * Background deploying ML models to edge devices or on-premises hardware in production environments
\n

 * Experience with workflow/orchestration tools such as Flyte, Airflow, Kubeflow, or Temporal
\n

 * Familiarity with GPU infrastructure management and inference optimization (TensorRT, Triton Inference Server)
\n

 * Experience with MLOps tools including experiment tracking (MLflow, Weights & Biases), feature stores (Feast, Tecton), and model registries
\n

 * Experience building RAG systems, vector databases (Pinecone, Weaviate, Milvus), or LLM deployment pipelines
\n

 * Background with computer vision model deployment, OCR/IDP systems, or document processing pipelines
\n

 * Experience coordinating with platform or infrastructure teams at larger organizations (e.g., working with central ML platform teams)
\n

 * Familiarity with event-driven architectures, IoT/UNS patterns, and real-time systems integration
\n

 * Knowledge of edge ML deployment, model optimization (quantization, pruning), or deploying models on resource-constrained devices
\n

 * Experience in regulated environments (NNPI/ITAR) and secure model/data governance
\n

 * Experience in hyper-growth startup environments, with demonstrated success balancing speed, ambiguity, and long-term system health
\n

 * Familiarity with enterprise systems such as ERP, MES, WMS, or PLM