Full-Stack Engineer (Mid-Level) - NiFi / Java

Torch.AI•Leawood, KS

1d•Hybrid

About The Position

Torch.AI is building the AI Control Layer for the United States Government, providing 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 multi-INT fusion, predictive movement analysis, targeting workflows, anomaly detection, and decision support for cross-domain operations. They seek candidates who can build systems operating under real constraints like degraded visibility, adversarial mis/disinformation, intermittent connectivity, low-SWaP hardware, and seconds-to-decision timelines, emphasizing operational AI that ships, runs, and is 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, with their AI Control Layer increasing the value of existing systems. They measure performance by accuracy, latency, and efficiency, focusing on craftsmanship and disciplined shipping. Collaboration with engineers, data experts, veterans, and mission practitioners is key, with opportunities to own meaningful work and see systems deployed in production quickly. The environment is fast-paced, entrepreneurial, and mission-driven, presenting new puzzles daily.

Requirements

B.S. or M.S. in Computer Science, Engineering, or related field.
3–6 years of experience in backend or full-stack development with a strong emphasis on backend systems.
Strong proficiency in Java and experience building backend services using frameworks such as Spring Boot or Micronaut.
Experience designing and implementing REST APIs and service integration layers.
Hands-on experience with Apache NiFi or similar data integration/orchestration tools.
Strong understanding of data pipelines, ETL processes, and data transformation patterns.
Experience working with SQL databases and familiarity with NoSQL systems.
Experience deploying services to cloud environments (AWS preferred) and using containerization (Docker).
Familiarity with Git workflows, CI/CD pipelines, and automated testing practices.
Ability to design, troubleshoot, and optimize distributed backend systems.

Nice To Haves

Experience with Kafka or event-driven / streaming architectures.
Familiarity with Airflow or other orchestration tools.
Experience with observability tooling (Prometheus, Grafana, ELK).
Exposure to microservices and cloud-native architectural patterns.
Experience supporting defense, intelligence, or other secure environments.
Exposure to AI/ML-enabled systems, data pipelines, or large-scale data processing platforms.
Basic familiarity with frontend technologies (React, TypeScript) for API integration support.

Responsibilities

Design and build backend systems that support mission-critical data processing, integration, and operational workflows.
Develop scalable services and APIs using Java (Spring Boot, Micronaut) and/or Python to support high-throughput, data-intensive applications.
Design, implement, and maintain data ingestion and orchestration pipelines using tools such as Apache NiFi.
Integrate and transform data across multiple sources, enabling reliable data movement, enrichment, and downstream consumption.
Optimize backend performance, including database queries, service latency, and data pipeline throughput.
Build and maintain RESTful APIs and service integration layers supporting distributed systems.
Collaborate with platform, DevOps, and security teams to deploy and operate services in cloud and hybrid environments.
Contribute to CI/CD pipelines, containerized deployments, and automated testing workflows.
Support troubleshooting and root-cause analysis across backend services and data pipelines.
Work with SQL and NoSQL data stores to design schemas and implement efficient data access patterns.
Provide backend support to frontend applications, ensuring reliable data delivery and API performance.
Document system architecture, data flows, and technical decisions in internal documentation systems.