H2O.ai

No-code AutoML platform for AI/ML

Senior Manager AI Engineering

Engineering LeadershipConfirmed live in the last 24 hours

Full-timeSydney NSW, AustraliaNo salary listed

About the Job

Requirements

At least 8 years of experience in data science, machine learning, AI engineering, or software engineering, including enterprise delivery experience.
At least 2 years of experience leading technical teams through direct people management or sustained technical-lead ownership of engineers and outcomes.
Experience hiring, mentoring, giving performance feedback, and developing engineers.
A track record of managing multiple concurrent enterprise engagements to time, quality, and budget.
A strong hands-on foundation in predictive machine learning, including feature engineering, model development, evaluation, and interpretability.
Experience building and deploying production AI systems, including application programming interfaces, backend services, or machine-learning-powered applications.
A solid understanding of generative AI systems, including large language models, small language models, retrieval-augmented generation, prompt engineering, evaluation, and agentic workflows.
Strong Python skills and production engineering practices, including testing, continuous integration and continuous delivery, debugging, version control, and system design.
Experience with cloud platforms and machine-learning operations or large-language-model operations concepts, including deployment, monitoring, and governance.
Ability to review technical implementations, guide architecture decisions, and coach engineers to make those decisions independently.
Strong customer-facing communication skills and comfort whiteboarding with engineers and presenting to executive stakeholders.

Responsibilities

Lead end-to-end delivery of AI, machine-learning, and generative-AI 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, match skills to accounts, and clear 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.
Prototype, review, and debug code, and make and defend architecture decisions.
Guide design and review of predictive machine-learning models, including time series, classification, and clustering, and generative-AI systems, including large language models, small language models, retrieval-augmented generation, prompt engineering, evaluation, and agentic workflows.
Ensure best practices across the machine-learning 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.
Act as the primary technical point of contact and escalation point for customers, balancing delivery execution with stakeholder management from engineer to executive level.
Partner with sales and pre-sales on scoping, estimation, statements of work, and expansion opportunities.
Collaborate with product and engineering teams to build reusable accelerators and reference architectures, and feed field learnings back into the roadmap.

Desired Qualifications

Experience with H2O products, including Driverless AI, H2O-3, H2O Wave, h2oGPTe, and H2O LLM Studio.
Prior experience in technical consulting, solutions engineering, or AI delivery roles.
Experience building or scaling a technical team or function, rather than just inheriting one.
Experience managing distributed teams across time zones.
Commercial exposure to scoping, statements of work, estimation, and accountability for delivery margin.
Experience leading workshops, hackathons, or enterprise AI enablement programmes.
Exposure to regulated industries, including financial services, healthcare, and insurance.
Experience designing or reviewing full-stack AI applications or platforms.
Contributions to Kaggle, open source, technical writing, or conference speaking.

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 www.h2o.ai.

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.
Act as the primary technical point of contact and escalation point for customers, balancing delivery execution with stakeholder management from engineer to executive level.
Partner with sales and pre-sales on scoping, estimation, statements of work and expansion opportunities.
Collaborate with product and engineering teams to build reusable accelerators and reference architectures, and feed field learnings back into the roadmap.

What We Are Looking For

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.

How to Stand Out From the Crowd

Experience with H2O products (Driverless AI, H2O-3, H2O Wave, h2oGPTe, H2O LLM Studio).
Prior experience in technical consulting, solutions engineering, or AI delivery roles.
Experience building or scaling a technical team or function, not just inheriting one.
Experience managing distributed teams across time zones.
Commercial exposure: scoping, SOWs, estimation, and accountability for delivery margin.
Experience leading workshops, hackathons, or enterprise AI enablement programmes.
Exposure to regulated industries (financial services, healthcare, insurance).
Experience designing or reviewing full-stack AI applications or platforms.
Contributions to Kaggle, open source, technical writing, or conference speaking.

Why H2O.ai?

Market leader in total rewards
Remote-friendly culture
Flexible working environment
Be part of a world-class team
Career growth

H2O.ai is committed to creating a diverse and inclusive culture. All qualified applicants will receive consideration for employment without regard to their race, ethnicity, religion, gender, sexual orientation, age, disability status or any other legally protected basis.

H2O.ai is an innovative AI cloud platform company, leading the mission to democratize AI for everyone. Thousands of organizations from all over the world have used our cutting-edge technology across a variety of industries. We’ve made it easy for people at all levels to generate breakthrough solutions to complex business problems and advance the discovery of new ideas and revenue streams. We push the boundaries of what is possible with artificial intelligence.

H2O.ai employs the world’s top Kaggle Grandmasters, the community of best-in-the-world machine learning practitioners and data scientists. A strong AI for Good ethos and responsible AI drive the company’s purpose.

Please visitwww.H2O.ai to learn more.

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For information about how H2O.ai collects, uses, and protects your personal information during the recruitment process, please review our Candidate Privacy Notice.

About the Company

H2O.ai

No-code AutoML platform for AI/ML

H2O.ai provides AI and ML solutions for businesses, with Driverless AI as its core AutoML platform. It automates feature engineering, model building, visualization, and interpretability to help users create predictive and generative models with minimal coding. The platform supports information retrieval, data labeling and processing, no-code deep learning, and emphasizes data security, plus a platform to develop, deploy, and share trusted AI apps. Its goal is to help organizations of all sizes accelerate data-driven decisions and scale secure AI deployment, differentiating through accessible AutoML, enterprise-grade security, and a strong talent base that includes Kaggle Grandmasters.

Company size: 201-500
Company stage: Series E
Total funding: $251.1M
Headquarters: Mountain View, CA
Founded: 2012

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