Summary
What you’ll impact
The AI Platform Senior Lead will own the architecture and technical direction of the organization’s enterprise AI platform, focusing on model integration, orchestration, retrieval, evaluation, and responsible AI guardrails. This senior individual-contributor role partners with AI Engineering, Data Engineering, Product Management, and Data Governance to set technical standards and mentor engineers.
Responsibilities
What you'll do
- Own the architecture and technical direction of the enterprise AI platform, including model access, orchestration, retrieval and grounding services.
- Design and build the integration layer between foundation models, internal data sources and the firm's AI applications.
- Establish evaluation infrastructure - offline test suites, golden datasets, regression testing and quality metrics - so model and prompt changes can be measured rather than guessed at.
- Build observability, logging, cost tracking and monitoring for AI workloads in production.
- Implement responsible-AI guardrails covering access control, data handling, confidentiality, auditability and output safety, in partnership with Data Governance and Information Security.
- Set engineering standards, design patterns and reusable frameworks that AI Engineering teams build against.
- Lead technical design sessions and own code review for platform components.
- Evaluate emerging models, tools and vendor platforms, and make build-versus-buy recommendations with a clear technical rationale.
- Partner with Product Management to translate product requirements into platform capabilities.
- Mentor engineers across the AI and data teams and raise the technical bar on AI engineering practice.
- Troubleshoot complex issues across the AI stack, from retrieval quality to latency and cost.
Requirements
What you’ll bring
- Bachelor's degree in Computer Science, Engineering, Data Science or a related field.
- Significant hands-on software or data engineering experience, including senior technical ownership of a platform or infrastructure area.
- Demonstrated experience building production applications on large language models - retrieval-augmented generation, tool and function calling, agentic workflows, or comparable patterns.
- Strong proficiency in Python, and solid SQL.
- Experience with model orchestration and AI application frameworks, and with vector or hybrid search infrastructure.
- Practical experience designing evaluation and testing approaches for non-deterministic systems.
- Hands-on experience with a major cloud platform - Azure, AWS or GCP - with Azure preferred, including CI/CD, version control, automated testing and monitoring.
- Working knowledge of AI security and governance concerns: prompt injection, data leakage, access control, PII and confidential data handling, auditability.
- Technical credibility and leadership presence, with the ability to set direction, review others' work and drive execution without direct authority.
- Strong communication skills, with the ability to explain technical tradeoffs to non-technical stakeholders.