AI Engineer
Location: On-site (Rosebank)
Employment Type: Full-time
Experience Level: Intermediate-Senior
Department: AI Delivery Team
About the Role
We're looking for an AI Engineer who works at the intersection of product strategy, hands-on engineering, and applied AI - and who understands that most of the value gets created before any code exists.
This role is for someone who doesn't just design solutions on a whiteboard - you define the problem space, hunt for the unlock nobody else has seen, design reusable solutions, and orchestrate AI agent teams to execute at scale. You'll own product outcomes end-to-end while actively building, prototyping, and evolving intelligent systems that solve real business problems.
You'll work closely with engineers, designers, stakeholders, and clients to translate ambiguous, messy, real-world problems into clear product direction and production-ready AI solutions. You won't wait for a well-scoped ticket. You'll walk into ambiguity and create structure through clear thinking and action.
What You'll Do
Hands-on AI Engineering
- Design, build, and maintain agentic AI systems (multi-step reasoning, tool use, orchestration, memory, autonomy)
- Orchestrate purpose-built agent teams — design how agents coordinate, what information flows where, and how parallel workstreams converge into coherent output
- Design reusable solution patterns — recipes and mapping functions that apply repeatedly within and across problem spaces
- Write clean, production-quality code to implement AI-driven features
- Build prototypes and proof-of-concepts quickly, then harden them for production
- Integrate LLMs, APIs, vector databases, tools, and external systems
- Implement evaluation, monitoring, and iteration strategies for AI systems
- Stay current with emerging AI patterns, frameworks, and best practices — our methods evolve fast, and you'll help shape them
Technical Collaboration
- Work closely with engineers to shape architecture and implementation decisions
- Review code and technical designs with a product-focused mindset
- Balance innovation with maintainability, scalability, and security
- Advocate for responsible, ethical, and user-centred AI usage
- Iterate on plans and ideas in the abstract before dropping into execution — challenge your own framing, stress-test approaches, ask better questions
What We're Looking For
Core Experience
- Proven experience in AI development
- 3-6 years experience in Engineering or Technical delivery/Software Development
- Engineering degree from any discipline is a major plus — mechanical, civil, electrical, industrial, chemical. Engineering teaches you how to think: systems, first principles, constraints, trade-offs, decomposition. Experience in industries where things get built physically: construction, mining, manufacturing, energy, infrastructure.
- Strong hands-on software development experience -you've built systems that solve real problems, not tutorial projects
- Exposure to data engineering and ML pipelines, DL environments, including model development workflows and basic MLOps practices (e.g. training, evaluation, and deployment)
Technical Skills
- Strong programming fundamentals (Python or similar preferred, but demonstrated problem-solving in any modern language)
- Experience building with LLMs and agentic frameworks (e.g. LangChain, LangGraph, CrewAI, AutoGen, custom agent architectures)
- Experience designing workflows involving: Tool calling and orchestration
- Memory and context management
- Multi-agent or multi-step reasoning
- Comfortable with APIs, cloud services, and modern application architectures
- Understanding of software quality, testing, and production readiness
- AI tooling, agentic workflows, and orchestration patterns are a strong plus — but developable if your foundation and drive are right. We coach the specifics.
Product & Thinking Skills
- First-principles problem-solving — you define the real problem, not the surface symptom
- Comfortable working in ambiguity and evolving requirements — that's where the value is, not a reason to wait
- Ability to explain complex technical concepts to non-technical stakeholders — in business terms, not jargon
- Outcome-driven mindset — focused on value and impact, not tools and frameworks
- Thinks in reusable patterns — not just solving this problem, but the class of problems it belongs to
- Questions the question before answering it
Nice to Have
- Consulting or client-facing delivery experience
- Has worked across multiple industries or domains — the scenic route is a feature
- Experience with UX research or discovery practices
- Familiarity with compliance, privacy, or responsible AI principles
How You Work
- You define the real problem before jumping to solutions — and you question the question before answering it