Senior Manager, AI Engineering
- Compensation
- Not disclosed
- Location
- Sydney
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 2+ years
- Category
- Not specified
- Posted
Skills
Must have
- 8+ years in data science, machine learning, AI engineering or software engineering, with enterprise delivery experience.
- 2+ years leading technical teams, either through direct people management or sustained tech-lead ownership of engineers and outcomes.
- Demonstrated experience hiring, mentoring, giving performance feedback and developing engineers.
- Track record of running multiple concurrent enterprise engagements to time, quality and budget.
- Strong hands-on foundation in predictive machine learning, including feature engineering, model development, evaluation and interpretability.
- Experience building and deploying production AI systems, including APIs, backend services or ML-powered applications.
- Solid understanding of GenAI systems including LLMs, SLMs, RAG, prompt engineering, evaluation and agentic workflows.
- Strong Python skills and production engineering practices (testing, CI/CD, debugging, version control, system design).
- Experience with cloud platforms and MLOps/LLMOps concepts (deployment, monitoring, governance).
- Ability to review technical implementations and guide architecture decisions, and to coach engineers to make those decisions independently.
- Strong customer-facing communication skills, comfortable moving between whiteboarding with engineers and presenting to executive stakeholders.
Full description
H2O.ai is on a mission to democratize AI for Good. As the world’s leading agentic AI company, H2O.ai converges Generative and Predictive AI to help enterprises and public sector agencies develop purpose-built Agents, SLMs, and solutions on their private data. With a focus on secure, compliant, and infrastructure-flexible Sovereign AI deployments, H2O.ai delivers solutions that align with the highest standards of data privacy and control.
Its open-source technology is trusted by over 20,000 organizations worldwide, including more than half of the Fortune 500. H2O.ai powers AI transformation for companies like AT&T, Commonwealth Bank of Australia, Wells Fargo, Bank of America, Workday, Progressive Insurance, and NIH.
For more information, visit .
About This Opportunity
We are looking for a Senior Manager, AI Engineering who builds things that matter. You will design and ship end-to-end AI solutions for some of APAC's most complex enterprise problems - spanning agentic AI systems, LLM applications, and production ML pipelines. This is a hands-on engineering role embedded within a customer-facing field team, meaning your work will be seen, used, and evaluated by real enterprises from day one.
You will work alongside Kaggle Grandmasters, ML engineers, and domain experts to deliver AI that goes beyond demos - into production, into workflows, and into measurable business outcomes.
This position is based in Sydney, Australia.
What You Will Do
- Lead end-to-end delivery of AI/ML and GenAI solutions for enterprise customers across industries such as financial services.
- Own delivery execution across multiple concurrent AI workstreams, ensuring scope, timelines, quality and business outcomes are met.
- Manage, mentor and grow a team of AI engineers and data scientists, owning hiring, onboarding, performance management and career development.
- Own staffing and capacity planning across engagements, matching skills to accounts and clearing blockers for the team.
- Manage technical delivery risks, dependencies and coordination across engineering, data science, product and customer teams.
- Partner with customers to translate business problems into technical AI solutions, including model design, architecture and deployment approach.
- Stay hands-on: prototype, review and debug code, and make and defend architecture decisions.
- Guide design and review of ML models (predictive ML, time series, classification, clustering) and GenAI systems (LLMs, SLMs, RAG, prompt engineering, evaluation, agentic workflows).
- Ensure best practices across the ML lifecycle: data preparation, feature engineering, training, evaluation, deployment, monitoring and governance.
- Raise the technical bar across the team through design and code reviews, pairing and structured feedback.