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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Software Engineer, AI-Augmented Development - **Company:** ACT, Inc - **Location:** Iowa City, IA, United States (Remote available) - **Experience:** Expert - **Salary:** $110,000.0 - **Contract:** Permanent contract - **Skills:** Query Performance, Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Computing Platforms, Cloud Computing, Code Generation, Code Review, Computer Programming, Data Security, Relational Databases, Distributed Systems, PostgreSQL, Node.Js, Object-Oriented Software Development, Software Engineering, Management of Software Versions, ReactJS, Claude Code, AI Coding Agents, Backend, Data Layers, Kubernetes, Graphql, Front End Software Development, Functional Programming, Api Design, NestJS - **Published:** October 22, 2026 - **Apply:** https://diversityjobs.com/career/18733114/Senior-Software-Engineer-Ai-Augmented-Development-Iowa-Iowa-City ## About the Role * Minimum of 5 years of progressively responsible software engineering experience. * Engineering Fundamentals + Object-oriented and/or functional programming fundamentals. + Data modeling and query performance reasoning, across relational and/or schema-driven data layers. + API design fundamentals: contracts, versioning, and backward compatibility, whether REST or schema-first (e.g., GraphQL). + Security fundamentals: authentication/authorization flows, common injection classes, and dependency risk. + Distributed systems fundamentals: failure modes, statelessness, and behavior under concurrent load. * Representative Technologies + A backend language/runtime, such as Node.js (NestJS), Java, or a comparable OOP/functional stack. + An API paradigm, such as GraphQL or REST, including schema design and versioning. + A modern frontend framework, such as React or a comparable component-based framework. + A relational database, such as PostgreSQL, or a comparable RDBMS. + Cloud/container infrastructure, such as AWS and Kubernetes, or comparable cloud-native infrastructure. + Claude Code, or a comparable AI-assisted coding tool, used in production. * Demonstrated, hands-on production use of Claude Code or a comparable AI-assisted coding tool - candidates should be able to describe specific instances where these tools materially changed their approach to a problem. * Track record of piloting a new AI-assisted development tool or technique and measuring its actual impact (time saved, defect rate change, or review burden) rather than just using it because it's available. * Demonstrated ability to critically evaluate AI-generated code before accepting it, with a specific example of catching an AI-generated error before it shipped. * Working knowledge of AI-specific security risk, e.g., unverified or malicious dependencies suggested by AI tools, injected logic in auto-accepted code; and habits for catching it before it reaches production. * Strong engineering fundamentals (programming, data modeling, API design, security, and distributed systems) that hold up independent of AI tooling; treats AI tools as an amplifier of skill. * Demonstrated ability to work from ambiguous specs, with sound judgment for when to escalate and when to proceed independently. * Experience working (or ability to work) across a mixed legacy and modern technology stack. * Experience owning delivery end to end, from specification through production, including writing specs clear enough for an AI tool to implement correctly. * Bachelor's degree in computer or data science, software engineering, artificial intelligence, or a related field, or an equivalent combination of education and experience from which comparable knowledge and abilities can be acquired. It's a plus if you have: * Demonstrated ability to influence engineering practices and colleagues outside of a direct reporting relationship, or interest in growing into this scope. * Experience evaluating or comparing multiple AI coding tools or models for team fit. * Familiarity with test administration or assessment delivery systems, or a comparable domain is valued as a plus alongside strong engineering fundamentals and AI-directed development skill. * Experience mentoring or setting technical standards across multiple teams. * Background in education technology or a similarly regulated, integration-heavy domain. * Experience building or extending eval or guardrail frameworks for AI-generated code. ## Description + Solve complex technical problems across the team's core systems, using AI-assisted approaches to move faster, including integrations with related platforms. + Contribute senior-level technical judgment to active modernization initiatives within the team, using AI tools to accelerate legacy-to-modern platform migrations. * AI Tooling Strategy & Adoption + Pilot new AI-assisted development tools, models, and techniques within the team (e.g., new agentic coding workflows, MCP integrations, updated model versions), evaluating them against real delivery work rather than vendor claims. + Propose and champion new use cases for AI in the team's engineering practice, backed by a measured before/after comparison - time saved, defect rate, or review burden. + Feed adoption learnings back into team practice, updating norms, playbooks, or tooling recommendations as new approaches prove out (or don't). * Deep Domain Knowledge + Apply deep familiarity with the team's platform architecture, history, and constraints to guide technical decisions, including where AI-assisted approaches can speed modernization work. * AI-Directed Development & Delivery Ownership + Direct AI coding tools and agents to implement features from specs, tickets, or requirements, and own end-to-end delivery of the feature from specification through production. + Review, test, and correct AI-generated code for correctness, security, and architectural fit; catch both subtle errors (a plausible-but-wrong calculation, an approach that won't scale) and obvious ones. + Act as a steward against AI-specific security and supply-chain risk, scrutinizing AI-suggested dependencies, flagging unfamiliar or unverified packages before they're accepted, and treating AI-generated code that touches auth, data access, or external integrations with elevated review. + Write specs detailed enough for an AI tool to implement correctly on the first or second pass; for ambiguous problems, exercise judgment on whether to escalate or proceed independently. + Maintain and extend the team's eval and guardrail practices that catch AI-introduced regressions before they reach production. * Analysis, Recommendations & Autonomy + Independently evaluate where AI-assisted development tools, primarily Claude Code, alongside comparable tools as needed, add genuine value, for example, scaffolding and legacy code comprehension, while applying extra human scrutiny to auth-adjacent or compliance-sensitive logic. + Recommend and refine engineering practices with a high degree of autonomy, guided by high-level direction and using independent judgment to accomplish goals. * Acts as a Resource for Colleagues + Proactively share and teach AI-assisted development practices with the team (code review norms, prompt approaches, and verification habits for AI-generated output)with the opportunity to extend this to other engineering teams as the role grows. + Operate as an individual-contributor role, building credibility and driving adoption of good practice through technical expertise and collaboration. * Program & Initiative Leadership (Growth Path) + As the role matures, lead or contribute to a cross-team initiative establishing shared, practical standards for responsible AI tool usage in engineering, including guidance on what's safe to share with external AI tools, and when AI-generated code warrants elevated review. + Participate in the engineering support rotation per the team's standard model. Role Progression: The following outlines key milestones and expectations for the first year in this role. 3-Month Mark * Completed onboarding on the team's platform architecture and AI tooling conventions; has shipped a few small features or fixes through AI-directed development, with work reviewed and merged at the team's normal quality bar. * Is closely working with other team members to understand production issues and system configurations, and has demonstrated at least one concrete instance of catching an AI-generated error or gap before it shipped. 6-Month Mark * Owns a feature or workstream end-to-end - writing the spec, directing AI-assisted implementation, and handling review and testing - with minimal day-to-day oversight from the lead. * Has contributed a concrete improvement to the team's AI-assisted development practice (a new guardrail, a refined spec template, a prompt pattern) that at least one other engineer has adopted. * Demonstrates a working understanding of the end-to-end architecture of the platforms they support, including how those platforms integrate with other systems across the broader technology ecosystem, and the underlying infrastructure architecture beneath them. 12-Month Mark * Has led a modernization effort or comparable initiative to completion, with AI-directed development visibly accelerating delivery versus a traditional approach. * Is beginning to extend influence beyond the team, if the opportunity has arisen - through early contributions to the cross-team AI standards initiative, informal mentoring, or a guardrail others have started using.