Senior AI Platform Engineer, VP / DIR 

We are looking for a Senior AI Platform Engineer to help build and scale the firm's AI platform. This role sits at the intersection of artificial intelligence, software engineering, and platform architecture, with responsibility for delivering the shared services and frameworks that enable teams across the firm to build, evaluate, deploy, govern, and operate AI applications safely and efficiently. 

You will work across foundational AI platform capabilities including agent runtimes and frameworks, evaluation systems, observability, guardrails, retrieval infrastructure, and governance controls. The role requires strong software engineering fundamentals, hands-on Python development, and the ability to turn rapidly evolving AI technologies into reliable, reusable, enterprise-grade platform services. 

This is initially a senior individual contributor role with substantial technical leadership responsibilities. The role may expand into broader leadership over time, including mentoring, team formation, and potential people management. 

Key Responsibilities 

• Design, build, and operate the firm’s AI platform: shared services, APIs, SDKs, and tooling that enable secure, consistent AI adoption. 

• Drive architecture across scalability, reliability, security, developer experience, and cost; define clear abstractions across application, framework, runtime, and infrastructure layers. 

• Build platform capabilities for LLM and agentic systems (orchestration, tool integration, memory, workflow execution, runtime management). 

• Develop retrieval and knowledge-access services supporting RAG and related patterns. 

• Implement evaluation frameworks to measure quality, reliability, safety, and business outcomes; use telemetry to prevent regressions and drive continuous improvement. 

• Build observability for tracing, metrics, logs, sessions, and execution history; deliver operational monitoring and diagnostics. 

• Implement guardrails for policy enforcement, risk mitigation, tenant isolation, and governance. 

• Write high-quality production code; establish strong engineering practices (testing, code review, CI/CD, documentation, operational ownership). 

• Operate reliable cloud-native services with monitoring, incident response, performance management, and capacity planning. 

• Lead complex technical initiatives end-to-end; mentor engineers and help shape platform standards and roadmaps. Potential people leadership as the team scales. 

Required Qualifications 

• 6+ years of software engineering experience with ownership of production services, platforms, or distributed systems. 

• Advanced proficiency in Python and strong software engineering fundamentals. 

• Deep knowledge of software architecture, service design, APIs, data modeling, concurrency, and distributed systems. 

• Experience building, deploying, and operating cloud-native services (Azure or similar). 

• Proficiency in testing strategies, CI/CD, observability, reliability engineering, and secure SDLC practices. 

• Proven track record leading ambiguous, cross-functional initiatives and delivering maintainable production systems. 

• Excellent written and verbal communication; able to explain architectural decisions to technical and non-technical stakeholders. 

Preferred Qualifications (Strong Plus)  

• Hands-on experience building production AI or generative AI platforms and services.  

• Experience with LLMs, agent frameworks, tool use, orchestration, memory, and retrieval-augmented generation architectures.  

• Experience designing AI evaluation frameworks, benchmarks, regression testing, or quality measurement systems.  

• Experience building guardrails, responsible AI controls, governance capabilities, or safety and reliability mechanisms.  

• Experience with agent observability, distributed tracing, telemetry, and execution-history storage and retrieval.  

• Practical Rust experience, particularly for high-performance, reliable, or systems-oriented platform components.  

• Experience designing shared developer platforms, SDKs, or self-service capabilities used by multiple engineering teams.  

• Experience mentoring engineers, acting as a technical lead, or helping grow an engineering team.  

• Exposure to financial services or another regulated environment is beneficial but not required.  

What Success Looks Like in This Role  

• Engineering teams can build and deploy AI solutions faster by using reliable, well-documented platform capabilities you help create.  

• Evaluation, observability, guardrail, and agent-runtime capabilities become reusable platform services adopted across multiple use cases.  

• The platform provides clear abstractions, production-grade reliability, and a strong developer experience without compromising governance requirements.  

• You become a recognised technical leader who shapes AI platform architecture, engineering standards, and delivery priorities. You help grow the team and can take on broader technical or people leadership responsibilities.