MLOps Engineer
Build and operate production ML infrastructure spanning training, deployment, serving, monitoring, data pipelines, and feedback-driven retraining. The role requires strong MLOps and DevOps experience, Python and SQL proficiency, and ownership of reliable cloud-based systems.
About the job
Responsibilities
- Build and evolve the MLOps platform and CI/CD workflows, including experiment tracking, model registries, packaging, automated training and retraining, deployment, and safe rollout and rollback.
- Operate reliable, scalable batch, streaming, and real-time model-serving infrastructure for models and digital twins.
- Build ML data and feature pipelines for telemetry, process and knowledge graphs, images, time-series data, conversations, and other production sources.
- Maintain feature-store capabilities and a lakehouse foundation using Apache Iceberg on Amazon S3, with data quality, lineage, versioning, and reproducibility.
- Build model observability and human-in-the-loop systems that capture production signals and expert corrections as ground truth for evaluation and retraining.
- Develop standardized tooling and workflows for reliable, reproducible ML development.
- Drive infrastructure, reliability, scalability, architecture, and technical direction from design through production.
- Collaborate with Process, Chemical, Materials, Simulation, Software, Data, and AI engineers.
Requirements
- 5+ years of relevant industry experience building production software, infrastructure, data, or machine learning systems.
- Experience building and operating machine learning systems in production with an MLOps or DevOps orientation.
- Strong DevOps fundamentals, including CI/CD, containers, Kubernetes, cloud infrastructure, and infrastructure as code.
- Proficiency in Python and SQL.
- Hands-on experience with MLflow or similar experiment-tracking, model-registry, and model-lifecycle tooling.
- Experience with Amazon S3, lakehouse technologies such as Apache Iceberg, and workflow orchestration tools such as Airflow or Dagster.
- Experience building pipelines for multimodal ML data, including images, time-series, structured, and semi-structured data.
- Familiarity with manufacturing systems, sensors, process automation, or other physical-world data systems.
- Strong problem-solving, data-quality, reliability, and technical communication skills.
- Bachelor's or master's degree in Computer Science, Data Engineering, Data Science, a related STEM field, or equivalent practical experience.
Nice-to-Haves
- Feature stores, human-in-the-loop systems, active learning, or data-labeling infrastructure.
- Robotics or robotic automation, including sensors, vision systems, or robotics data.
- Operating ML systems in manufacturing or other physical-world environments.
- Internal tools for expert feedback, labeling, model evaluation, or model interaction.
- Shared ML infrastructure or platforms used across multiple teams or applications.
Compensation
- Salary range: $200,000–$250,000 USD.
- Compensation also includes equity and benefits.
Skills
MLOps, DevOps, CI/CD, Kubernetes, Docker, Python, SQL, MLflow, Amazon S3, Apache Iceberg, Airflow, Dagster, Feature Stores, Infrastructure As Code
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