2 months ago
Singapore, SingaporeStaff+
Responsibilities
- Own AI solution delivery from problem framing and feasibility assessment through architecture, implementation, production release, and continuous improvement.
- Design enterprise architectures integrating AI capabilities with applications, APIs, data platforms, identity, security, and operational controls.
- Build and review critical software components while establishing coding, testing, quality, cost, speed, and maintainability standards.
- Design and implement agentic applications using agentic harnesses, retrieval-augmented generation, MCP, function and tool calling, structured outputs, and multi-agent workflows.
- Engineer production AI platforms covering inference architecture, model routing, caching, semantic retrieval, vector databases, evaluation pipelines, observability, and cost optimization.
- Define evaluation criteria, test sets, feedback loops, and monitoring for accuracy, reliability, safety, latency, cost, and business impact.
- Implement responsible AI controls including guardrails, human oversight, access controls, privacy protections, and auditability.
- Collaborate with product owners, business leaders, architects, data specialists, delivery teams, security, privacy, risk, and legal partners.
- Mentor engineers, develop reusable patterns and reference implementations, and improve AI engineering practices.
- Communicate technical options, risks, and recommendations to technical and non-technical stakeholders, including senior clients and business leaders.
Requirements
- Typically 10–12 years of professional experience in software engineering, solution architecture, platform engineering, or related enterprise technology roles.
- Demonstrated experience 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 at least one enterprise application stack such as Java, .NET, or TypeScript/Node.js.
- Experience delivering solutions on at least one major cloud platform, including AWS, Microsoft Azure, or Google Cloud.
- Working knowledge of enterprise data integration, SQL, search and 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 without relying solely on formal authority.
- Clear communication, commercial judgment, and the 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, or governance experience is useful but not essential.
- Familiarity with AI application frameworks such as Langraph or PydanticAI, model platforms such as Azure Foundry or AWS Bedrock, Kubernetes, and infrastructure as code is useful but not essential.
- A degree in computer science, engineering, or a related discipline, or equivalent professional experience.
Benefits
- Marsh offers a diverse, inclusive, and flexible work environment.
- The role follows a hybrid work model with remote flexibility and collaboration in the office or onsite with clients at least three days per week.
- Office-based teams identify at least one anchor day per week for the full team to work together in person.
Tech Stack
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