MLOps Engineer

Salary

Job Purpose/Summary

TheMLOpsEngineer designs, builds, andoperatesscalable machine learning systems that transform spatial-temporal and sensor-derived data into reliable ML workflows. This role spans the full ML lifecycle ingest, normalization, and feature engineering pipelines through distributed training and evaluation to low-latency inference and operational integration.

Working across data infrastructure and deployment environments, the engineer operationalizes experimental models into reproducible, observable, and scalable systems. They ensure ML pipelines, containerized workloads, and CI/CD processes are robust, automated, and designed for real-world operational demands.

In close collaboration with data scientists, geophysicists,and cross-functional engineering teams, this role translates research-grade algorithms into resilient services. As part of a fast-moving, government-funded technologybusiness, theMLOpsEngineer operates with high ownership in a low-ceremony, applied research environment, bringing structure, repeatability, and best practices to mission-driven sensor analytics systems.

Duties andResponsibilities

Design, build, andoperatescalable ML and data pipelines for spatial-temporal and sensor-driven datasets.
Operationalizedata science algorithms into reliable, distributed ML workflows covering feature extraction, training, evaluation, inference, and model lifecycle management.
Implement andmaintaincontainerized 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 andmaintainCI/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 ofMLOpspractices within an applied research team, bringing structure, repeatability, and best practices to evolving environments.

Qualifications

Minimum

3+ years of experience inMLOps, 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 asPyTorch, TensorFlow, orKerasinto 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. Citizenshiprequired.

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 pipelinese.g.Kafka, Spark, Flink, or Sedona feedinglakehouse-style open table formatse.g.Iceberg or Delta.
Experience with SQL query enginese.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 withTimescaleDBand/orPostGIS, integrating ML outputs into operational databases.
DevOps experience with Helm andGitOpstooling.
Background in defense, cybersecurity, space, or other mission-driven sensor analytics environments.

Working conditions

Employees may be called upon toparticipatein in-person meetings, trainings, or company functions atKnowmadicsoffices or other designated locations. Travel in support of business operations may also berequired, and employees are expected tocomply withthese 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.