Senior Software Engineer, AI-Augmented Development

ACT is hiring a Senior Software Engineer to deliver software using AI-assisted development while applying engineering judgment to review, test, and correct AI-generated work. The role owns delivery from specification through production, pilots and evaluates AI tools, and shares effective practices with the team, with potential to help establish cross-team standards.

Responsibilities:

• 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
• 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)
• 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
• 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
• 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
• 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
• 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

Requirements:

• Applicants must be authorized to work in the U.S. without the need for visa sponsorship
• Minimum of 5 years of progressively responsible software engineering experience
• 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
• 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
• 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