Generative artificial intelligence is reshaping how we serve clients and run the firm. In the Chief Data and Analytics Office, you will lead the delivery of enterprise-grade generative artificial intelligence products and platforms with strong governance and controls. You will partner across machine learning, cloud engineering, and site reliability engineering to ship resilient solutions with clear return on investment. This is a hands-on leadership role for someone who enjoys building at scale and operating in real production environments.
As a Lead, Generative AI Engineering in the Chief Data and Analytics Office, you will lead the design, delivery, and continuous improvement of production generative artificial intelligence products and reusable backend application programming interfaces used across the firm. You will guide technical direction from experimentation through production hardening, ensuring reliability, scalability, performance, and responsible artificial intelligence controls. You will work closely with cross-functional partners to define measurable outcomes and drive execution against them. You will mentor engineers and raise the bar on engineering excellence and operational rigor.
Job responsibilities
- Lead the design and delivery of production generative artificial intelligence products and reusable backend application programming interfaces for firmwide adoption
- Architect scalable systems that combine large enterprise datasets with large language and multimodal models
- Set technical direction for model-enabled services, including quality, latency, throughput, and cost targets
- Partner with cloud engineering and site reliability engineering teams to deliver resilient architectures, observability, and operational readiness
- Drive translation of research concepts into production-ready capabilities through evaluation, iteration, and hardening
- Establish engineering standards for reliability, security, and responsible artificial intelligence controls across the product lifecycle
- Own delivery planning and execution, including risks, dependencies, and stakeholder communication
- Define and manage objectives and key results aligned to business outcomes, adoption, and return on investment
- Mentor and develop engineers through coaching, technical reviews, and role modeling best practices
- Troubleshoot critical production issues, lead root-cause analysis, and implement long-term preventative improvements
Required qualifications, capabilities, and skills
- PhD in a quantitative discipline such as Computer Science, Mathematics, or Statistics, or equivalent practical experience
- 7+ years of experience in machine learning engineering and/or applied software engineering delivering production systems
- 3+ years of technical leadership experience, including leading delivery for complex cross-functional initiatives
- Demonstrated experience owning enterprise machine learning services, including reliability, incident management, and service-level outcomes
- Strong fundamentals in statistics, optimization, and machine learning theory with applied expertise in natural language processing and/or computer vision
- Hands-on experience implementing distributed, multi-threaded, scalable systems (for example Ray, Horovod, or DeepSpeed)
- Proven ability to design and scale service-oriented architectures and application programming interfaces with high availability and performance requirements
- Experience defining success metrics and writing clear objectives and key results aligned to business expectations
- Strong judgment to align technical decisions with governance, risk, and control requirements for responsible artificial intelligence
- Excellent communication and stakeholder management skills, with ability to influence across senior technical and business audiences
Preferred qualifications, capabilities, and skills
- Experience designing and implementing machine learning pipelines using directed acyclic graph frameworks (for example Kubeflow, DVC, or Ray)
- Experience building batch and streaming microservices exposed via gRPC and/or GraphQL
- Demonstrable experience with parameter-efficient fine-tuning, quantization, and quantization-aware fine-tuning for large language models
- Experience with multimodal large language model use cases (text plus image, speech, or video)
- Experience with advanced prompting and reasoning approaches such as chain-of-thought, tree-of-thought, or graph-of-thought
- Experience establishing evaluation frameworks and production monitoring for model quality, safety, and drift
- Experience building reusable platforms that enable other teams to ship model-enabled products faster.
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