AI Engineer - Sydney at Marsh McLennan

Sydney, New South Wales, Australia -

Full Time

Start Date

Immediate

Expiry Date

27 Sep, 26

Salary

0.0

Posted On

29 Jun, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI Engineering, Machine Learning, Generative AI, LLMs, RAG, Agentic Workflows, Cloud Architecture, MLOps, LLMOps, Data Engineering, Prompt Engineering, Python, API Development, CI/CD, Containerization, Vector Databases

Industry

Professional Services

Description

Company: Oliver Wyman Description: Who We Are At Oliver Wyman, a Marsh (NYSE: MRSH) business, we bring deep industry insight, bold innovation, and a collaborative approach that cuts through complexity to help organizations navigate their most defining transformative moments. As a business of Marsh, we work alongside the world’s leading experts across risk, reinsurance and capital, people and investments, and management consulting. Together with Marsh Risk, Guy Carpenter, and Mercer, we help organizations build resilience and competitive advantages from every angle. With annual revenue of $27 billion and more than 95,000 colleagues in 130 countries, Marsh helps build the confidence to thrive through the power of perspective. For more information, visit oliverwyman.com. Job Overview We are seeking an AI Engineer to design, build, deploy, and scale enterprise AI, machine learning, and Generative AI solutions that create measurable business value. This role sits at the intersection of AI engineering, data engineering, cloud architecture, and solution delivery. The successful candidate will help develop production-ready AI systems, including LLM-powered applications, retrieval-augmented generation (RAG), agentic workflows, and data-driven architectures across cloud and hybrid environments. In this role, you will partner with business and technology stakeholders to identify high-value opportunities, shape solution designs, and deliver AI-enabled products that improve productivity, support better decision-making, enhance operational efficiency, and strengthen customer outcomes. You will also contribute to the organization’s broader AI capability by evaluating emerging technologies, sharing best practices, and helping advance responsible AI adoption. This is an opportunity to work collaboratively across multidisciplinary teams and help shape AI engineering standards, frameworks, and delivery practices in a fast-evolving environment. What You Will Do AI solution design and delivery Design, develop, and deploy enterprise AI, machine learning, and Generative AI solutions that address complex business challenges and deliver measurable value. Translate business needs into scalable technical architectures, solution designs, and implementation plans. Build production-ready AI applications using large language models (LLMs), RAG, agentic workflows, and advanced machine learning techniques. Support the full solution lifecycle, from ideation and prototyping through deployment, monitoring, and continuous enhancement. Develop reusable assets, frameworks, accelerators, and delivery practices that improve efficiency and consistency across initiatives. Generative AI and agentic systems Design and implement LLM-powered applications using leading AI platforms and models, such as OpenAI, Anthropic Claude, Llama, and other foundation models. Develop agentic workflows and multi-agent systems using orchestration frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, or similar tools. Apply prompt engineering, tool/function calling, memory management, retrieval techniques, and coordination patterns to improve solution performance. Evaluate and optimize model and application behavior through experimentation, benchmarking, fine-tuning, and prompt refinement. Stay informed about developments in Generative AI and agentic technologies and assess their relevance to business needs. Data engineering and AI platform enablement Design, develop, and maintain scalable data pipelines that support AI, machine learning, and analytics workloads. Integrate structured and unstructured data from internal systems and external sources. Build and optimize retrieval architectures using relational databases, NoSQL systems, vector databases, and knowledge repositories. Work closely with data engineering and architecture teams to support data quality, reliability, governance, and accessibility. Contribute to the development of modern data platforms that enable scalable AI deployment and operationalization. Cloud engineering, MLOps, and LLMOps Design and deploy AI solutions across cloud platforms including AWS, Microsoft Azure, and Google Cloud Platform. Build and maintain MLOps and LLMOps pipelines to support development, deployment, monitoring, experimentation, and lifecycle management. Implement CI/CD, infrastructure as code, automated testing, and deployment automation to improve reliability and scalability. Use containerization and orchestration technologies such as Docker and Kubernetes to support enterprise deployment. Ensure solutions meet performance, scalability, resilience, and operational support requirements. Responsible AI, governance, and risk Embed responsible AI principles, governance controls, and security considerations throughout the AI lifecycle. Support monitoring frameworks, model evaluation methods, and guardrails that promote safe and reliable deployment. Ensure compliance with organizational policies, regulatory requirements, privacy standards, and information security guidelines. Identify and help manage risks related to model performance, bias, explainability, security, and operational resilience. Contribute to the development and adoption of AI governance standards and best practices. Stakeholder collaboration Work closely with business stakeholders, technology teams, data scientists, product owners, and project teams to deliver AI-enabled transformation initiatives. Facilitate requirements-gathering sessions, solution-design workshops, and technical discussions with both technical and non-technical audiences. Communicate complex technical concepts clearly and tailor recommendations for different stakeholders. Support project planning, estimation, prioritization, and delivery within agile and cross-functional teams. Contribute to knowledge sharing, capability building, and innovation across the broader AI and data community. Innovation and capability development Evaluate emerging technologies, frameworks, and trends in AI, machine learning, and cloud engineering. Identify opportunities to improve existing solutions and introduce new capabilities that strengthen business outcomes. Support internal capability-building initiatives, technical learning sessions, and communities of practice. Contribute to thought leadership, reusable intellectual property, and innovation accelerators. What you will bring 4–5 years of experience in AI engineering, machine learning, data science, software engineering, data engineering, or a related technology field. Demonstrated experience designing, developing, and deploying AI, machine learning, or Generative AI solutions in production environments. Hands-on experience with LLMs, prompt engineering, RAG, model evaluation, and AI application development. Experience building and deploying scalable solutions on cloud platforms such as AWS, Microsoft Azure, and/or Google Cloud Platform. Experience working with AI frameworks, orchestration tools, and agentic architectures such as LangChain, LangGraph, LlamaIndex, CrewAI, or similar technologies. Experience building and managing data pipelines, ETL/ELT processes, and data integration workflows using structured and unstructured data. Experience with relational, NoSQL, and vector databases in support of enterprise AI and analytics solutions. Familiarity with software engineering best practices, including API development, microservices, version control, automated testing, CI/CD, and containerization. Experience with MLOps and/or LLMOps practices, including model lifecycle management, monitoring, experimentation tracking, deployment, and observability. Experience translating business requirements into technical solutions and working effectively with cross-functional teams. Ability to communicate technical concepts, solution designs, and implementation approaches to both technical and non-technical audiences. Exposure to responsible AI, model governance, AI risk management, security controls, and regulatory considerations in an enterprise environment is an advantage. Consulting, professional services, or client-facing delivery experience is preferred but not required. Oliver Wyman is a business of Marsh (NYSE: MRSH), a global leader in risk, reinsurance and capital, people and investments, and management consulting, advising clients in 130 countries. With annual revenue of over $27 billion and more than 95,000 colleagues, Marsh helps build the confidence to thrive through the power of perspective. For more information, visit oliverwyman.com, or follow us on LinkedIn and X. Marsh is committed to creating a diverse, inclusive and flexible work environment. We aim to attract and retain the best people and embrace diversity of age, background, disability, ethnic origin, family duties, gender orientation or expression, marital status, nationality, parental status, personal or social status, political affiliation, race, religion and beliefs, sex/gender, sexual orientation or expression, skin color, or any other characteristic protected by applicable law. Applications will only be considered from candidates who have the appropriate approval to work in Australia. Successful applicants will be required to complete a Criminal & Bankruptcy check prior to commencing of employment. Marsh is committed to hybrid work, which includes the flexibility of working remotely and the collaboration, connections and professional development benefits of working together in the office. All Marsh colleagues are expected to be in their local office or working onsite with clients at least three days per week. Office-based teams will identify at least one “anchor day” per week on which their full team will be together in person.

Responsibilities

Design, build, and scale enterprise AI and Generative AI solutions, including LLM-powered applications and agentic workflows. Partner with stakeholders to deliver AI-enabled products that improve operational efficiency and business decision-making.