[Hiring] Senior AI-Native Engineer @Intetics

Role Description

Intetics Inc., a global technology company providing custom software application development, distributed professional teams, software product quality assessment, and “all-things-digital” solutions, is seeking a highly skilled and experienced Senior AI-Native Engineer to join our dynamic team on a full-time basis.

Our partner ships AI-powered selling and sales intelligence products in a market that moves faster every day. We are focused on quality, functionality, and speed to delivery, and the engineers who thrive here don't treat AI tools as a productivity add-on — they've fundamentally changed how they work, using agentic development workflows to go from specification to production-ready code faster than they could before, without sacrificing correctness or quality.

This is not a role for someone who favors traditional software development, or is curious about AI-assisted development and plans to explore it. It's a role for someone who already works this way — who writes clear, machine-readable specifications that feed the agentic development system, uses agentic development tools as standard equipment, and applies genuine critical judgment to what these tools produce before it ships. You'll build systems and features that power real customer outcomes in a B2B SaaS platform at scale, and you'll do it inside a delivery model where human judgment is the differentiating input, not the bottleneck.

If you want to work on meaningful software with teammates who hold the bar high on both speed and craft, this role is for you.

What You'll Do

• Agentic Development Systems
  • Lead the definition of spec and implementation standards for your feature area — write executable SDD/BDD specs that set the quality bar for your team, and keep them current as implementation evolves.
  • Use agentic coding tools (Claude Code, Cursor, Copilot, or equivalents) as standard equipment across the full SDLC, and drive adoption of AI-first development practices within your team.
  • Apply critical judgment to AI-generated code and agentic attestations before merge — assess correctness, edge cases, security implications, and alignment with the spec; your review is a real gate, not a formality.
  • Shape the team's agentic development norms: spec standards, attestation practices, and responsible AI tool use.
  • Act as an advocate for Agentic & Spec-Driven Development (SDD) — influence engineering practices, demonstrate real-world production delivery without manual coding bottlenecks, and align distributed teams around AI-first norms.
• Feature Delivery
  • Lead the technical execution of complex features and cross-team systems, owning them end-to-end from spec through production.
  • Design, implement, and ship solutions that are reliable, maintainable, and built to perform in high-volume, high-availability SaaS environments.
  • Anticipate where complexity will compound — surface systemic risks early before they become problems for your team or your customers.
• Code Quality & Review
  • Review AI-generated code and agentic attestations with the rigor the team depends on; catch what automation misses and raise the bar for what "good" looks like.
  • Orchestrate unit and integration test development that verifies real behavior, not just execution paths.
  • Contribute to and help define the team's technical standards across all facets of the AI SDLC.
• Collaboration & Mentorship
  • Serve as a go-to technical voice on engineering practices within your team; mentor L1 and L2 engineers and make the people around you better.
  • Work closely with product managers, designers, and QA engineers to understand requirements and build the right thing, not just the specified thing.
  • Communicate clearly in writing — in specs, in code reviews, in async updates — to keep cross-functional and globally distributed teams aligned.
  • Participate in agile and team ceremonies, including Sprint Review, Program Reviews, and other technical presentations.

What Success Looks Like

• You lead complex features from spec to production using agentic workflows, and can speak concretely to how that changed delivery speed and code quality.
• Your specs are clear, your tests are meaningful, and your PRs tell a coherent story — your output is easy for teammates to review, extend, and maintain.
• AI-generated code you've attested has not introduced regressions or security issues past your review; your attestation means something.
• Junior engineers on your team are measurably better because of your mentorship and code review.
• You anticipate where complexity will compound and surface it early — your team doesn't get surprised by problems you saw coming.

Qualifications

• 5+ years of experience developing enterprise or SaaS software in production environments.
• Personal fluency with agentic coding tools (Claude Code, Cursor, Copilot, or equivalents) — you use these in your daily work and real delivery scenarios, not as an experiment.
• Proven experience creating and using agentic development systems with genuine rigor — spec-first workflows, attestation practices, BDD/SDD — this is how we work, not a practice you're learning on the job.
• Demonstrated track record of shipping complex features independently, end-to-end.
• Proficiency in modern backend/full-stack technologies, with strong experience in Golang, PHP, or Node.js/TypeScript ecosystem, along with solid unit/integration testing skills.
• Experience with RESTful API design and consumption.
• Working knowledge of SQL and NoSQL data stores.
• Experience producing and maintaining automated test suites (unit, integration, or end-to-end).
• Experience working in Agile delivery environments with CI/CD pipelines.
• Strong written communication skills; able to write clear specs, meaningful code review feedback, and async updates for a global team.

Preferred Qualifications

• Experience with cloud platforms (AWS or equivalent) and microservices architectures.
• Familiarity with containerization, infrastructure-as-code, or observability tooling.
• Experience developing or integrating with AI/ML features in a production SaaS product.
• Background in B2B SaaS, CRM, or revenue technology.