
AI Engineer
4 weeks ago
We're not just talking about AI - we're building one of the first enterprise-grade LLM implementations that delivers real, measurable value to compliance and legal teams TODAY.
For the last 2 years, we've been transforming how organisations handle compliance from a painful checkbox exercise into a smooth, intelligent process. Our AI-powered platform is helping legal, compliance, and marketing teams revolutionise their operations with 80% faster reviews and 4x productivity boosts. (And yes, we triple-checked those numbers)
Why is this exciting?
Because we're surfing the biggest wave in enterprise tech - the AI revolution. As the regtech industry rockets toward a $50 billion future, we're proudly flying the Australian flag on the global stage.
Think about it: Design has Canva. Projects have Trello. Now, Compliance has Haast.
The Role
As an AI Engineer at Haast, you'll design and implement hybrid recall methods that blend cutting-edge search databases with large language models. You'll play a key role in shaping how our platform detects compliance risks in real time, designing intelligent, scalable systems that power critical decision-making for enterprise teams. We're looking for someone passionate about pushing the boundaries of AI and search technology to solve complex compliance challenges.
This is an ideal role for someone who wants to take ownership of high-impact systems in a product-led, AI-first company and help define how cutting-edge models are applied to real business problems.
Impact you'll make
- Own the design and implementation of hybrid search methods combining vector databases and LLMs
- Lead the optimisation of LLM pipelines and retrieval strategies for high-recall compliance detection
- Architect and maintain search infrastructure for scalable, low-latency query handling
- Design benchmarking systems and drive continuous model evaluation and tuning
- Work across structured and unstructured data to surface hidden compliance risk signals
- Partner with product and engineering to bring new ML features to life, end to end
Must Have:
- Deep expertise in machine learning and/or search engineering, especially in production environments
- Proficiency in Python and writing clean, maintainable code
- Hands-on experience with LLMs, RAG architectures, and real-time inference systems
- Experience designing, deploying, and maintaining ML systems in production
- Experience working across back-end systems and search infrastructure
- Experience building NLP evaluation systems
- Data Engineering Experience
- Experience with search technologies (Vespa, OpenSearch)
- A background building benchmarking systems
- FastAPI framework knowledge
- Startup or high-growth environment experience
- AWS cloud platform expertise
- Research background in ML or search
- Experience with DevOps and CI/CD tools
Growth and Impact:
- Leading the evolution from dull, traditional SaaS to AI-powered workflows (because nobody ever said "I love manually reviewing disclaimers")
- Build enterprise-grade AI technology that major institutions actually trust (and love)
- Empowering humans to focus on strategic work they love by eliminating tedious compliance reviews
- Creating technology that builds customer trust and protects consumer rights
- Remote for exceptional candidates, preferably Sydney-based with flexible work arrangements (at least 2 days a week)
- Competitive salary and equity (ESOP) - we believe in sharing the success
- Rapid career growth - high performers get real opportunities, not just high fives and a monthly pizza party
- Work with the cutting-edge AI technologies that your current employer claimed you would be
- Collaborative, engineering-driven culture focused on learning and development
At Haast, we believe bold ideas come from diverse perspectives. We're committed to building a team that reflects the world we work in; across gender, background, orientation, ability, and experience. Even if you don't tick every box, we'd still love to hear from you. If this role sparks your interest, reach out - let's chat.
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