Responsibilities
- Manage a team of AI and machine learning engineers and managers, guiding the end-to-end development of production-ready AI systems, including both traditional ML models and applications powered by large language models.
- Provide high-level technical expertise in system architecture and modeling decisions, collaborating closely with clients and business leaders, including active participation in pre-sales activities such as proposal development and solution design.
- Establish standardized practices for integrating context engineering, agent frameworks, and model fine-tuning into all AI solutions.
- Design, build, and optimize production AI systems—including classical machine learning, LLM-based applications, and agentic architectures—with a focus on scalability, reliability, and efficient inference.
- Develop and standardize context engineering methods, including prompt engineering, retrieval-augmented generation (RAG), vector databases, memory handling, and function calling, to ensure models receive timely and relevant inputs.
- Lead the creation of robust agent harnesses, including orchestration layers, evaluation systems, safety controls, and monitoring tools, to ensure reliable and measurable LLM operations in production.
- Oversee the development and maintenance of fine-tuning pipelines, including training data selection, supervised fine-tuning, model evaluation, and deployment, to tailor models for specific client applications.
- Design and implement AI solutions primarily on Google Gemini Enterprise and Vertex AI platforms, with additional use of Microsoft AI Foundry and AWS Bedrock when required by client environments.
- Lead, mentor, and grow a team of AI and ML engineers, setting technical standards and promoting knowledge sharing and best practices.
- Support business development by contributing to pre-sales efforts, including scoping projects, building prototypes, and presenting technical architectures to potential clients.
- Supervise the creation of both classical and modern machine learning models—such as forecasting, recommendation, and deep learning systems—selecting appropriate methods based on business needs.
- Collaborate with senior leadership to influence the organization's generative AI strategy, including architecture frameworks, tool selection, and platform evolution.
Compensation
The estimated base compensation for this role starts at $200,000 (NYC location). Individual compensation is determined by skills, qualifications, and experience.
Work Arrangement
On-site — NYC, Los Angeles
Team
Leading a team of AI & machine learning engineers and managers
Other
- The estimated base compensation for this role starts at $200,000 (NYC location). Individual compensation is determined by skills, qualifications, and experience.
- This role is eligible for competitive benefits.
