Software Engineer (Mid-Level) - Data Pipelines & Event Processing

Torch.AI•Leawood, KS

2d•Hybrid

About The Position

Torch.AI is building the AI Control Layer for the United States Government, focusing on foundational data and intelligence infrastructure to make sense of complex, multi-domain data at machine speed. Their platform—ORCUS, NEXUS, and HALO—is accredited, deployed, and operating across enterprise clouds and tactical edge environments, supporting various critical operations like multi-INT fusion, predictive movement analysis, and decision support. The company seeks candidates who want to build systems that operate under real constraints such as degraded visibility, adversarial mis/disinformation, intermittent connectivity, low-SWaP hardware, and seconds-to-decision timelines, emphasizing operational AI that ships, runs, and gets used to strengthen U.S. defense readiness and protect national interests. Torch.AI believes the government must own its data, decision environment, and reasoning infrastructure, and their AI Control Layer enhances existing systems rather than replacing them. They prioritize accuracy, latency, and efficiency, fostering a fast-paced, entrepreneurial, and mission-driven environment where engineers collaborate with data experts, veterans, and mission practitioners to deploy systems quickly. This role specifically focuses on building production-grade software systems for ingesting, processing, and enriching large volumes of real-world data to support mission-critical decision making. The engineer will develop scalable data pipelines and backend services to transform diverse, multi-source information into structured, actionable insights, with a strong emphasis on reliability, adaptability, and operational security. Systems are designed for continuous operation, handling evolving data sources, and functioning within controlled environments that demand careful management of access patterns, attribution, and compliance constraints.

Requirements

B.S. or M.S. in Computer Science, Engineering, or related field.
3–6 years of professional software engineering experience.
Strong proficiency in Python (preferred) or similar backend programming languages.
Experience building production systems for data ingestion, integration, or processing.
Experience working with APIs, automated data access techniques, and distributed systems.
Strong understanding of data pipeline patterns, including ETL/ELT workflows.
Experience working with structured and semi-structured data formats (JSON, CSV, Parquet).
Familiarity with SQL and NoSQL databases and query optimization.
Experience designing systems that operate reliably under variable data availability and latency conditions.
Familiarity with cloud environments (AWS preferred) and containerization (Docker).
Experience with CI/CD pipelines and modern software development practices.
Understanding of secure software development practices and controlled data access patterns.
Strong problem-solving skills and ability to operate in fast-paced, mission-driven environments.

Nice To Haves

Experience with workflow orchestration tools (Airflow, NiFi) or similar systems.
Experience with streaming or event-driven architectures (Kafka or equivalent).
Familiarity with large-scale data ingestion, indexing, or search systems.
Experience working with geospatial, temporal, or multi-source datasets.
Exposure to data enrichment, entity resolution, or relationship mapping techniques.
Experience implementing controlled or privacy-aware data acquisition techniques.
Familiarity with infrastructure patterns that support identity protection, request management, or traffic routing in distributed systems.
Experience supporting defense, intelligence, or other regulated environments.
Familiarity with distributed systems and microservices architectures.
Exposure to observability tooling (logging, monitoring, metrics).

Responsibilities

Design, build, and maintain backend services and pipelines that acquire and process data from diverse data sources.
Develop automated data access workflows using APIs, web interfaces, and distributed systems while adhering to controlled access and usage constraints.
Implement data transformation, enrichment, and normalization processes to support downstream analytics and mission applications.
Build systems that monitor and respond to real-world events by collecting, correlating, and updating relevant data streams.
Develop data fusion capabilities that combine multiple sources to improve data completeness, context, and usability.
Apply operational security practices to data acquisition and processing workflows, including controlled access patterns, traffic shaping, and protection of system and infrastructure.
Implement safeguards that reduce attribution risk and ensure responsible interaction with external data sources.
Collaborate with platform, data, and security teams to ensure systems meet compliance, auditability, and mission requirements.
Optimize performance, scalability, and resilience of data acquisition and processing systems.
Contribute to CI/CD pipelines, containerized deployments, and automated testing workflows.
Troubleshoot production issues and perform root-cause analysis across distributed systems.
Document system behavior, data flows, and integration patterns to support maintainability and operational use.
Participate in code reviews and contribute to engineering standards and best practices.