Senior Software Engineer

Must be able to work at our office in Orlando, Florida.

Company Description

Aptitude AI builds and deploys AI agents and automation systems that streamline complex workflows for organizations across industries. The company focuses on administrative automation such as scheduling, task management, and communications to help teams work more efficiently. Aptitude AI also delivers “Spatial Workflow” solutions using augmented reality to support maintenance, operations, and training for frontline workers. In addition, the company creates custom AI-powered process automation, ranging from digital assistants to spatially aware AR guides. Its solutions are designed to help customers save time, reduce errors, and scale operations effectively.

Role Description

The Senior Software Engineer will design, build, and maintain backend services and AI-driven workflow automation solutions in close collaboration with product and AI teams. Day-to-day work includes implementing robust APIs, integrating AI agents into existing systems, improving performance and scalability, and ensuring security and reliability of production services. The engineer will participate in architecture discussions, code reviews, and technical decision-making, while mentoring other developers and contributing to best practices and documentation. This is a remote contract role, requiring proactive communication, strong ownership of deliverables, and the ability to work effectively with a distributed team.

Qualifications

• Strong foundation in Computer Science and Software Development, including algorithms, data structures, and system design.
• Proficiency in Programming and Object‑Oriented Programming (OOP) principles, with experience in at least one modern language (e.g., Python, Java, C#, Go).
• Hands‑on experience in Back‑End Web Development, including RESTful APIs, microservices, and working with relational and/or NoSQL databases.
• Experience designing and maintaining production systems, focusing on reliability, scalability, and observability.
• Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and containerization/orchestration tools (e.g., Docker, Kubernetes).
• Experience integrating or working with AI/ML services, agents, or automation frameworks is preferred.
• Comfort with modern development practices such as version control (Git), CI/CD, automated testing, and code review.
• Effective written and verbal communication skills, with the ability to collaborate in a remote, cross‑functional environment.
• Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
• Prior experience in senior or lead engineering roles, including mentoring and technical leadership, is an asset.

The Ideal Candidate

Beyond the qualifications above, here's what separates a good fit from a great one:

• You read code better than most people write it. Much of our code moves through AI coding agents (Claude Code, Codex, and similar) operated by engineers at every level. You're the human backstop: you review agent‑generated PRs quickly and skeptically, catch hallucinated APIs, security gaps, and architectural drift, and coach junior developers on directing agents effectively - rather than merging whatever compiles.
• You own architecture, not just tickets. Given an ambiguous product goal, you can scope it into a technical roadmap - service boundaries, data models, migration paths, build‑vs‑buy calls - and defend those decisions in review. You've designed systems that outlived your involvement.
• You've shipped in regulated environments. Some of our deployments run in HIPAA‑regulated healthcare settings. You've built or operated systems that handle PHI: access controls, audit logging, encryption in transit and at rest, environment separation, and delivering alongside compliance requirements without grinding the roadmap to a halt.
• You build agents, not just call APIs. Hands‑on AI agent development: prompt design and iteration, harness and scaffolding construction, tool orchestration, retrieval systems including RAG and Graph RAG, and - critically - deterministic governance around probabilistic models: guardrails, policy gates, structured outputs, and verification layers that make agent behavior auditable and safe to run in production.
• You verify; you don't assume. You treat any output - human or agent - as unverified until tested. You instinctively build evals, CI gates, and observability so failures surface in staging, not in front of a customer.

This opportunity is much larger than just building automations for companies. You will be brought into a startup positioned to positively impact a large and very well established industry.