MLOps Engineer

Knowmadics

MLOps Engineer designing reliable ML workflows for transforming sensor data into operational systems. Collaborating with scientists and engineers in government-funded technology business.

Posted 5/29/2026full-timeRemote • Kansas, Oklahoma, Texas • 🇺🇸 United StatesMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

AirflowAWSCloudCyber SecurityKafkaKerasKubernetesNumpyPandasPostGISPostgresPySparkPythonPyTorchRaySparkSQLTensorflow

About the role

Key responsibilities & impact

Design, build, and operate scalable ML and data pipelines for spatial-temporal and sensor-driven datasets.
Operationalize data science algorithms into reliable, distributed ML workflows covering feature extraction, training, evaluation, inference, and model lifecycle management.
Implement and maintain containerized ML workloads in cloud-native environments.
Integrate model outputs into downstream serving systems and analytical platforms to support web-based applications and operational decision-making.
Develop and maintain CI/CD pipelines for ML and data services.
Collaborate closely with data scientists to operationalize experimental models into reproducible, observable, and scalable production systems.
Take ownership of MLOps practices within an applied research team, bringing structure, repeatability, and best practices to evolving environments.

Requirements

What you’ll need

Minimum: 3+ years of experience in MLOps, ML Engineering, Data Engineering, or closely related roles building and running ML/data pipelines.
Strong Python data and ML stack experience, including tools such as Polars/Pandas, PyArrow, PySpark, NumPy/SciPy.
Experience integrating models built with frameworks such as PyTorch, TensorFlow, or Keras into scalable pipelines.
Demonstrated experience working with temporal data, ideally including sensor-derived signals.
Practical CI/CD experience for ML/data services using Git-based workflows.
Experience working in AWS or similar cloud environments.
Experience running containerized ML or data workloads in Kubernetes.
Experience collaborating closely with data scientists to integrate algorithms.
Eligible to obtain a U.S. Security Clearance – U.S. Citizenship required.
Preferred: Direct 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 alternative event-driven autoscaling approaches.
Experience building event-driven or streaming pipelines e.g. Kafka, Spark, Flink, or Sedona feeding lakehouse-style open table formats e.g. Iceberg or Delta.
Experience with SQL query engines e.g. 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

Comp & perks

Employees may be called upon to participate in in-person meetings, trainings, or company functions at Knowmadics offices or other designated locations.
Travel in support of business operations may also be required, and employees are expected to comply with these obligations as part of their position.
Physical requirements may include sitting or standing for extended periods, working with computers and technical equipment, and occasionally lifting or moving materials or tools.

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

MLOpsML EngineeringData EngineeringPythonPolarsPandasPyArrowPySparkNumPySciPy

Soft Skills

collaborationownershipbest practicesstructurerepeatability

Certifications

U.S. Security Clearance