2 months ago
Sydney, AustraliaStaff+
Responsibilities
- Own AI solution delivery from problem framing and feasibility assessment through architecture, implementation, production release, and continuous improvement.
- Design enterprise architectures connecting AI capabilities with applications, APIs, data platforms, identity, security, and operational controls.
- Develop or review critical software components and establish coding, testing, quality, cost, and maintainability standards.
- Build production agentic applications using retrieval-augmented generation, MCP, function/tool calling, structured outputs, and multi-agent workflows.
- Design inference architectures, model routing, caching, semantic retrieval, vector databases, evaluation pipelines, observability, and cost optimization.
- Define evaluation criteria, test sets, observability, and feedback loops for accuracy, reliability, safety, latency, cost, and business impact.
- Implement responsible AI guardrails, human oversight, access controls, and auditability with security, privacy, risk, and legal partners.
- Collaborate with product owners, business leaders, architects, data specialists, and delivery teams on technical roadmaps.
- Mentor engineers, create reusable patterns and reference implementations, and communicate recommendations to technical and non-technical stakeholders.
Requirements
- Typically 10–12 years of professional experience in software engineering, solution architecture, platform engineering, or related enterprise technology roles.
- Strong record designing and delivering production enterprise applications, integrations, or digital platforms in complex, regulated, or security-conscious environments.
- Recent hands-on experience implementing AI-enabled solutions such as agentic applications, retrieval-augmented generation, intelligent workflow automation, or machine-learning services.
- Strong software engineering fundamentals including API design, distributed systems, automated testing, version control, CI/CD, observability, and secure development practices.
- Proficiency in Python and practical experience with Java, .NET, or TypeScript/Node.js.
- Experience delivering on at least one major cloud platform, with depth in one environment preferred over superficial coverage of AWS, Microsoft Azure, and Google Cloud.
- Working knowledge of enterprise data integration, SQL, search/retrieval, and structured and unstructured information.
- Understanding of model and application evaluation, prompt and context design, privacy, security, responsible AI controls, and production monitoring.
- Experience leading technical work across multidisciplinary teams, mentoring engineers, and influencing architecture or engineering standards.
- Clear communication, commercial judgment, and ability to translate ambiguous business needs into feasible technical approaches and delivery plans.
- Consulting, professional services, client-facing technology delivery, regulated-industry, enterprise risk, compliance, governance, containerized workload, Kubernetes, or infrastructure-as-code experience is useful but not essential.
- Familiarity with AI application frameworks such as Langraph or PydanticAI, or model platforms such as Azure Foundry or AWS Bedrock, is useful but not essential.
- A degree in computer science, engineering, or a related discipline, or equivalent professional experience.
Benefits
- Hybrid work with flexibility to work remotely and collaborate in the office; colleagues are expected to work in their local office or onsite with clients at least three days per week.
- Diverse, inclusive, and flexible work environment.
- Employment requires appropriate approval to work in Australia and completion of a Criminal & Bankruptcy check before commencing.
Tech Stack
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