MLOps Engineer

This is a fully remote position, open to applicants in Kansas, +2 more states.

📋 Description

• Design, develop, and manage scalable machine learning and data pipelines for spatial-temporal and sensor-driven datasets.

• Transform data science algorithms into dependable, distributed ML workflows that encompass feature extraction, training, evaluation, inference, and model lifecycle management.

• Implement and oversee containerized ML workloads in cloud-native settings.

• Incorporate model outputs into downstream serving systems and analytical platforms to facilitate web-based applications and support operational decision-making.

• Create and sustain CI/CD pipelines for ML and data services.

• Work collaboratively with data scientists to translate experimental models into reproducible, observable, and scalable production systems.

• Take charge of MLOps practices within an applied research team, introducing structure, repeatability, and best practices to dynamic environments.

⛳️ Requirements

• Minimum: 3+ years of experience in MLOps, ML Engineering, Data Engineering, or related roles involving the construction and management of ML/data pipelines.

• Strong experience with the Python data and ML stack, including tools such as Polars/Pandas, PyArrow, PySpark, and NumPy/SciPy.

• Proven experience integrating models developed with frameworks like PyTorch, TensorFlow, or Keras into scalable pipelines.

• Demonstrated experience with temporal data, preferably including sensor-derived signals.

• Practical experience with CI/CD for ML/data services utilizing Git-based workflows.

• Experience in AWS or comparable cloud environments.

• Familiarity with running containerized ML or data workloads in Kubernetes.

• Experience working closely with data scientists to integrate algorithms effectively.

• Must be eligible to obtain a U.S. Security Clearance – U.S. Citizenship is required.

• Preferred: Hands-on experience with sensor datasets such as seismographic data, cellular sensor modalities, RF survey data, or GPS devices.

• Experience deploying and scaling ML workloads in Kubernetes using KEDA or other event-driven autoscaling methods.

• Experience in building event-driven or streaming pipelines such as Kafka, Spark, Flink, or Sedona that feed lakehouse-style open table formats like Iceberg or Delta.

• Familiarity with SQL query engines such as Trino, DuckDB, or Athena.

• Experience selecting and operating orchestration frameworks such as Airflow, Dask, Ray, or Spark for scalable ML workloads.

• Strong PostgreSQL experience, ideally with TimescaleDB and/or PostGIS, integrating ML outputs into operational databases.

• DevOps experience with Helm and GitOps tooling.

• Background in defense, cybersecurity, space, or other mission-driven sensor analytics environments.

🏝️ Benefits

• Employees may be required to attend in-person meetings, training sessions, or company events at Knowmadics offices or other designated locations.

• Travel to support business operations may also be necessary, and employees are expected to fulfill these obligations as part of their role.

• Physical requirements may include sitting or standing for extended durations, working with computers and technical equipment, and occasionally lifting or moving materials or tools.