Software Engineer, Python, Backend Developer

About Aiki Tech

Aiki Tech builds secure generative AI products for the public sector, with initial applications in public safety and education. Our platform is configurable to agency workflows and built from the ground up to meet the security and privacy standards those agencies must satisfy.

Generative AI is advancing faster than established engineering playbooks, particularly in public-sector environments where accuracy, security, privacy, and reliability carry real consequences. Aiki Tech works at the leading edge of applying these capabilities to public safety and education. An early-career engineer who joins us will gain direct experience turning new GenAI capabilities into production software that agencies can trust.

We have completed a SOC 2 Type II Audit, operate government-cloud environments in both AWS Gov Cloud and Microsoft Azure Government, maintain a CJIS-aligned security program, and hold third-party FERPA and COPPA certifications. Compliance is part of how we build, not a checklist added at the end.

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This is an early-career position for someone with a strong graduate-level computer science foundation and zero to two years of professional software engineering experience. Your strongest experience may come from graduate coursework, research, internships, a teaching or research assistantship, open-source work, or substantial personal projects. Professional experience is welcome but not required.

You will work directly with current GenAI models, evaluation methods, retrieval, guardrails, cloud services, and production observability while learning from experienced engineers and progressively taking ownership of production components. This is not a research-only or prompt-writing position. You will write Python, build backend services, test system behavior, work with cloud infrastructure, diagnose failures, document your work, and learn how to build software for regulated public-sector environments.

• Build, test, and maintain Python backend services, APIs, and data-processing pipelines.
• Contribute to LLM-powered applications on leading public-cloud platforms, including work involving prompt design, retrieval, structured outputs, guardrails, model evaluation, and observability.
• Create and maintain automated tests, evaluation datasets, and regression checks for accuracy, latency, reliability, and failure handling.
• Integrate commercial and open-weight models, embedding services, databases, and internal platform components.
• Assist with real-time and near-real-time audio, streaming transcription, call metadata, and event-driven processing.
• Contribute to cloud deployments across AWS and Microsoft Azure, including government-cloud environments, under the guidance of experienced engineers.
• Use Git, code review, CI/CD, logging, monitoring, and issue tracking as part of the normal development lifecycle.
• Follow secure-development practices involving least-privilege access, secrets management, data-retention controls, encryption, testing, and documented change management.
• Help prepare technical documentation and supporting evidence for SOC 2 and CJIS-alignment work.
• Investigate defects and production issues, explain what happened, and help implement durable fixes.
• Learn directly from agency requirements and help translate them into reliable software behavior.

• Master's or doctoral degree in Computer Science, completed before the employment start date.
• Zero to two years of professional software engineering experience. Relevant internships, research assistantships, and substantial graduate engineering projects are valued and may constitute a candidate's primary experience.
• Strong Python programming skills and a solid understanding of data structures, algorithms, object-oriented design, and software-debugging fundamentals.
• At least one substantial software project that you can explain in depth, including the problem, architecture, code you personally wrote, testing approach, tradeoffs, and results. The project may come from coursework, research, an internship, open-source work, employment, or independent development.
• Practical exposure to LLM APIs or applied machine learning through coursework, research, internships, or projects.
• Experience building or consuming APIs and working with structured data.
• Familiarity with Git, automated testing, and collaborative software-development practices.
• Basic familiarity with at least one major public-cloud platform such as AWS, Microsoft Azure, or…