Explore 4 python aws developer jobs from 2 hiring companies in Winfield, KS, US. Filter by remote, salary, experience, and company, then open any listing to apply.

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Python Aws Developer Jobs

Knowmadics

Syms Strategic Group

Onsite

Remote

Job Purpose/Summary 
 
The Geospatial Software Engineer designs, builds, and operates scalable geospatial systems that transform complex spatial-temporal and sensor-derived data into analytics, visualizations, and web-based applications. This role spans the full data path from curated datasets and geospatial processing pipelines through to low-latency APIs and analyst-facing, map-based tools. 
  
Working across backend infrastructure and front-end visualization, the engineer transforms spatial-temporal data and develops robust analytics. They ensure that data pipelines, databases, and web applications are performant, reliable, and designed for real-world operational use. 
  
In close collaboration with data scientists, geophysicists, and cross-functional engineering teams, this role translates advanced research and sensor analytics into scalable systems. As part of a fast-moving, government-funded technology business, the Geospatial Software Engineer operates with high ownership in an applied research environment focused on delivering impactful, mission-driven solutions. 
 
Duties and Responsibilities 
<ul>
<li>Design and build scalable geospatial data pipelines from curated data </li>
</ul>
<ul>
<li>Develop and operationalize spatial-temporal analytics, including movement characterization, clustering, and advanced spatial transformations. </li>
</ul>
<ul>
<li>Translate complex geospatial workflows into reliable, low-latency APIs that power web-based analytical tools. </li>
</ul>
<ul>
<li>Build and maintain interactive, map-based applications for analysts exploring sensor-derived, spatial-temporal data. </li>
</ul>
<ul>
<li>Deploy and operate services in a cloud-native environment using containerized infrastructure. </li>
</ul>
<ul>
<li>Collaborate with data scientists and domain experts to operationalize advanced analytics into robust systems. </li>
</ul>
<ul>
<li>Take ownership of features from backend ingestion through to front-end visualization in an applied research environment. </li>
</ul>
 
Qualifications 
 
Minimum: 
<ul>
<li>3+ years of hands-on geospatial software engineering experience. </li>
</ul>
<ul>
<li>Strong Python geospatial experience, including libraries such as GeoPandas, Shapely, PyProj, and similar tooling. </li>
</ul>
<ul>
<li>Solid understanding of spatial modelling, projections, zonal statistics, and spatial transformations. </li>
</ul>
<ul>
<li>Experience working with spatial-temporal datasets. </li>
</ul>
<ul>
<li>Experience building ETL/data pipelines loadingdata into operational databases such as PostgreSQL with PostGIS/TimescaleDB. </li>
</ul>
<ul>
<li>Experience developing full-stack applications using Python-based backends, FastAPI/Pydantic preferred, and modern JavaScript front-end frameworks with React. </li>
</ul>
<ul>
<li>Experience building geospatial visualizations using libraries such as Cesium, Mapbox/MapLibre, or Lonboard. </li>
</ul>
<ul>
<li>Experience working in AWS or similar cloud environments. </li>
</ul>
<ul>
<li>Experience deploying containerized applications using Docker and familiarity with Kubernetes. </li>
</ul>
<ul>
<li>Eligible to obtain a U.S. Security Clearance – U.S. Citizenship required </li>
</ul>
Preferred: 
<ul>
<li>Direct experience processing geophysical, sensor, or seismographic data. </li>
</ul>
<ul>
<li>Experience developing CI/CD pipelines for data services using Git-based workflows and infrastructure-as-code principles. </li>
</ul>
<ul>
<li>Experience with event-driven or streaming data systems e.g. Kafka, Spark, Flink, or Sedona. </li>
</ul>
<ul>
<li>Experience with lakehouse-style analytical tables e.g. Iceberg or Delta  </li>
</ul>
<ul>
<li>Experience with SQL query engines e.g. Trino, DuckDB, or Athena </li>
</ul>
<ul>
<li>Strong production experience with PostgreSQL with PostGIS and TimescaleDB extensions at scale. </li>
</ul>
<ul>
<li>Experience working with cloud-native geospatial architectures and cloud-optimized formats. </li>
</ul>
<ul>
<li>Experience scaling geospatial workloads in Kubernetes-based environments. </li>
</ul>
<ul>
<li>Experience applying ML/AI techniques, e.g. Bayesian methods and neural networks, to spatial-temporal datasets. </li>
</ul>
<ul>
<li>Background in defense, cybersecurity, space, or other mission-driven sensor analytics environments. </li>
</ul>
Working conditions 
 
<ul>
<li>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. </li>
</ul>
 
 
Physical requirements 
 
May include sitting or standing for extended periods, working with computers and technical equipment, and occasionally lifting or moving materials or tools. 
 
Direct reports 
 
None

Software Engineer, Geospatial

Software Engineer

3+ years geospatial software engineering; strong Python geospatial stack; ETL pipelines; full-stack Python + React; AWS; Docker/Kubernetes; eligible for U.S. security clearance.

Job Purpose/Summary 
 
The MLOps Engineer designs, builds, and operates scalable 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 technology business, the MLOps Engineer operates with high ownership in a low-ceremony, applied research environment, bringing structure, repeatability, and best practices to mission-driven sensor analytics systems. 
 
Duties and Responsibilities 
 
<ul>
<li>Design, build, and operate scalable ML and data pipelines for spatial-temporal and sensor-driven datasets. </li>
</ul>
<ul>
<li>Operationalize data science algorithms into reliable, distributed ML workflows covering feature extraction, training, evaluation, inference, and model lifecycle management. </li>
</ul>
<ul>
<li>Implement and maintain containerized ML workloads in cloud-native environments. </li>
</ul>
<ul>
<li>Integrate model outputs into downstream serving systems and analytical platforms to support web-based applications and operational decision-making. </li>
</ul>
<ul>
<li>Develop and maintain CI/CD pipelines for ML and data services. </li>
</ul>
<ul>
<li>Collaborate closely with data scientists to operationalize experimental models into reproducible, observable, and scalable production systems. </li>
</ul>
<ul>
<li>Take ownership of MLOps practices within an applied research team, bringing structure, repeatability, and best practices to evolving environments. </li>
</ul>
 
Qualifications 
 
Minimum: 
<ul>
<li>3+ years of experience in MLOps, ML Engineering, Data Engineering, or closely related roles building and running ML/data pipelines. </li>
</ul>
<ul>
<li>Strong Python data and ML stack experience, including tools such as Polars/Pandas, PyArrow, PySpark, NumPy/SciPy. </li>
</ul>
<ul>
<li>Experience integrating models built with frameworks such as PyTorch, TensorFlow, or Keras into scalable pipelines. </li>
</ul>
<ul>
<li>Demonstrated experience working with temporal data, ideally including sensor-derived signals. </li>
</ul>
<ul>
<li>Practical CI/CD experience for ML/data services using Git-based workflows. </li>
</ul>
<ul>
<li>Experience working in AWS or similar cloud environments.  </li>
</ul>
<ul>
<li>Experience running containerized ML or data workloads in Kubernetes. </li>
</ul>
<ul>
<li>Experience collaborating closely with data scientists to integrate algorithms. </li>
</ul>
<ul>
<li>Eligible to obtain a U.S. Security Clearance – U.S. Citizenship required. </li>
</ul>
Preferred: 
<ul>
<li>Direct hands-on experience with sensor datasets such as seismographic data, cellular sensor modalities, RF survey data, or GPS devices. </li>
</ul>
<ul>
<li>Experience deploying and scaling ML workloads in Kubernetes using KEDA or alternative event-driven autoscaling approaches. </li>
</ul>
<ul>
<li>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. </li>
</ul>
<ul>
<li>Experience with SQL query engines e.g. Trino, DuckDB, or Athena </li>
</ul>
<ul>
<li>Experience selecting and operating orchestration frameworks such as Airflow, Dask, Ray, or Spark for scalable ML workloads. </li>
</ul>
<ul>
<li>Strong PostgreSQL experience, ideally with TimescaleDB and/or PostGIS, integrating ML outputs into operational databases. </li>
</ul>
<ul>
<li>DevOps experience with Helm and GitOps tooling. </li>
</ul>
<ul>
<li>Background in defense, cybersecurity, space, or other mission-driven sensor analytics environments. </li>
</ul>
 
Working conditions 
 
<ul>
<li>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. </li>
</ul>
 
Physical requirements 
 
May include sitting or standing for extended periods, working with computers and technical equipment, and occasionally lifting or moving materials or tools. 
 
Direct reports 
 
None

MLOps Engineer

3+ years in MLOps/ML/data engineering; Python stack; PyTorch/TensorFlow/Keras; AWS; Kubernetes; CI/CD; sensor data; eligible for U.S. security clearance.

Senior Cloud Computing Engineer

Design, deploy, and manage AWS infrastructure; implement CI/CD; ensure security/compliance; remote collaboration with VA.

Senior Systems Engineer (Production Support)

Senior Systems Engineer

8+ years in systems engineering or related field; 3+ years SQL; cloud and automation experience; security/compliance knowledge.

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