Applied AI Engineer - Anz

Cognition Cognition · Coding AI · Sydney, Australia · Sales

This role focuses on deploying and integrating AI software agents (like Devin) into enterprise engineering workflows to drive adoption and productivity. It involves embedding with teams, architecting agentic workflows, leading workshops, optimizing agent configurations, quantifying impact, and scaling learnings into playbooks.

What you'd actually do

  1. Embed with enterprise engineering teams to drive deep, lasting adoption of Devin — owning outcomes, not just onboarding
  2. Architect and implement agentic workflows across engineering, QA, support, data, and product — identifying where AI creates the highest leverage and building toward it
  3. Lead interactive programs for enterprise engineering teams (live workshops, pair programming sessions)
  4. Guide customers through installing, configuring, and optimizing Devin and its associated tools (DeepWiki, MCP integrations, etc.)
  5. Pair-program on live production problems to demonstrate high-value usage patterns and accelerate the team's applied AI fluency

Skills

Required

  • 3+ years as a software engineer, technical consultant, deployment strategist, forward deployed engineer, solutions engineer or similar roles
  • strong coding proficiency (Python, JavaScript/TypeScript, or similar)
  • Proven ability to communicate complex technical topics to diverse audiences
  • Proven track record of driving technical adoption and measurable impact inside engineering organizations
  • Strong commercial instincts
  • Excellent verbal and written communication skills
  • Demonstrated ability to learn and adapt exceptionally fast

Nice to have

  • Degree in a STEM field or equivalent hands-on experience
  • led developer enablement, platform adoption, or internal AI modernization initiatives
  • deployed or integrated LLM or agent-based systems in production settings
  • previously founded or joined early-stage startups where autonomy and execution speed were critical
  • energized by seeing a team's velocity compound after working with them — and you engineer those outcomes deliberately

What the JD emphasized

  • building end-to-end software agents
  • deploy it
  • agentic workflows
  • drive the kind of measurable productivity gains
  • strong engineering fundamentals
  • working directly with customers
  • driving product adoption
  • scale those learnings
  • strong coding proficiency
  • Proven ability to communicate complex technical topics to diverse audiences
  • Proven track record of driving technical adoption and measurable impact inside engineering organizations
  • Strong commercial instincts
  • deployed or integrated LLM or agent-based systems in production settings
  • seeing a team's velocity compound

Other signals

  • building end-to-end software agents
  • deploying AI modernization at the enterprise level
  • integrating agentic workflows into how they actually build and ship
  • driving measurable productivity gains