AI/ML Consulting Manager

Location: On-site in Sydney, Australia; travel to work with customers on-site up to 25% of the time.

Company

Snowflake provides a data cloud platform and partner ecosystem used to deliver AI agents, machine learning models, and full-stack applications from ideation through production.

What you will do

• Lead, coach, and develop a team of technical architects across Australia and New Zealand, including performance feedback and career development.
• Serve as a billable technical lead and delivery advisor across customer engagements, achieving a utilisation target through architecture guidance, technical reviews, project oversight, and customer advisory work.
• Own delivery outcomes across Snowflake architects and partner resources by translating customer requirements into engineering plans, milestones, priorities, and acceptance criteria.
• Guide customers from problem definition and solution design through development, evaluation, deployment, and operational handover for AI agents and generative AI applications, including retrieval-augmented generation and enterprise tool integration.
• Build customer relationships, coordinate stakeholders, manage expectations, escalate risks, and resolve delivery issues.
• Review solution quality across grounding, safe tool use, security, governance, evaluation, observability, technical debt, testing, regressions, and support burden; partner with Product Management and Engineering on delivery dependencies and product feedback.

Requirements

• 8+ years of relevant experience preferred in customer-facing technical consulting, solution architecture, or AI/ML delivery, with responsibility for complex enterprise engagements.
• Experience managing and developing technical professionals through coaching, performance management, career development, and collaborative knowledge sharing.
• Experience owning customer outcomes across internal and partner delivery teams, translating requirements into engineering plans, coordinating stakeholders and resources, and resolving escalations.
• Thorough understanding of the generative AI application lifecycle, including use-case framing, model selection, prompt and context design, agent orchestration, tool integration, evaluation, and production delivery.
• Strong understanding of production practices for LLM applications and AI agents, including evaluation, regression testing, deployment, observability, guardrails, human oversight, reliability, traditional machine learning, and MLOps.
• Cloud architecture knowledge and experience with at least one public cloud platform, such as AWS, Azure, or Google Cloud, including assessment of Snowflake and complementary technologies against customer requirements.
• Technical foundation in Python, APIs, and frameworks for LLM applications and AI agents, with design and code-review judgement to coach architects, identify technical debt and test gaps, and balance delivery speed, maintainability, and support cost.
• Clear communication with technical and executive audiences, sound judgement in customer expectations and delivery trade-offs, and a degree in computer science, engineering, mathematics, or a related field, or equivalent experience.

Nice to have

• Experience delivering generative AI applications and AI agents on Snowflake, including integration with enterprise data, APIs, and partner ecosystem tools.
• Experience improving consulting delivery through reusable architecture patterns, evaluation approaches, and AI-assisted workflows.
• Experience with agent orchestration frameworks, retrieval and vector search, or generative AI evaluation tools; additional data science and traditional machine learning experience is welcomed.
• Industry expertise in financial services, retail, or manufacturing, with the ability to connect technical decisions to customer priorities.

Culture & Benefits

• AI-native, experimental working environment focused on using emerging capabilities to improve how work gets done.
• Low-ego culture that values curiosity, collaboration, shared expertise, and challenging ordinary thinking.
• Customer-facing role with on-site customer travel of up to 25%.
• Opportunity to influence product priorities and reusable delivery practices through customer and field insights.