Jobs at Torch.AI

Build trusted relationships with government mission customers, coordinate deployments, onboarding, training, issue resolution, and adoption. Track outcomes, usage, risks, and mission feedback while communicating customer insights to engineering and leadership. Prepare status updates, adoption reports, executive summaries, and renewal materials. Support growth opportunities, post-deployment reviews, and continuous improvement across secure, mission-driven environments.

Designs and leads manual and automated testing across user interfaces, backend services, data pipelines, and end-to-end workflows. Builds automation suites, supports CI/CD quality gates, performs regression, integration, performance, and security testing, and validates ETL and data-fusion pipelines. The role coordinates defect triage, root-cause analysis, release readiness, quality metrics, and risk communication while partnering with product and engineering teams.

Supports CI/CD pipelines, infrastructure-as-code workflows, containerized environments, cloud deployments, monitoring, and deployment troubleshooting. Documents operational procedures and assists with secure infrastructure practices across AWS, Azure, or GCP. The role is designed for an early-career engineer with foundational Linux, scripting, Git, Docker, and DevOps knowledge, with opportunities to develop Kubernetes, observability, vulnerability scanning, and isolated-cloud expertise.

Design and develop Java backend services, REST APIs, cloud-ready microservices, database solutions, CI/CD pipelines, and containerized deployments. The role involves improving reliability and performance, troubleshooting environments, documenting technical decisions, and collaborating across teams. Candidates need an active TS/SCI clearance, Java backend experience, Spring Boot or Micronaut expertise, SQL and NoSQL knowledge, orchestration experience, and familiarity with AWS and containerization.

Designs and implements production machine-learning workflows for semantic search, classification, entity resolution, retrieval, embeddings, and RAG. Builds reliable AI services, integrates models with data and backend systems, evaluates mission performance, and supports deployment, monitoring, versioning, and MLOps practices. The role also contributes to architecture decisions and may support defense, intelligence, or mission-focused environments.