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 deployed across enterprise clouds and tactical edge environments, supporting various 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, and low-SWaP hardware, with seconds-to-decision timelines. This role is for operational AI that ships, runs, and is actively used to strengthen U.S. defense readiness and protect national interests. Torch.AI emphasizes that the government must own its data and reasoning infrastructure, and their AI Control Layer enhances existing systems rather than replacing them. They prioritize accuracy, latency, and efficiency, and foster a fast-paced, entrepreneurial, and mission-driven environment where engineers collaborate with data experts, veterans, and mission practitioners to deploy systems quickly. Successful candidates are builders who ship and iterate, are comfortable with messy data and unclear requirements, prioritize utility over elegance, care about explainability and determinism, move quickly while respecting security boundaries, and measure success by operational impact.

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.