Director of AI Development Location: New York, NY, United States Date Posted:Jan 6, 2025 The American Arbitration Association is an equal opportunity employer (EEO) and considers all employees and applicants for positions without regard to race, color, religion, gender, sexual orientation, national origin, age, disability, marital status, or status as a covered veteran in accordance with applicable federal, state and local laws. If you are unable to complete your application, you may request a disability accommodation and submit your information through an alternative method by contacting the Human Resources Department via email- [email protected] . Description Named one of the 50 best nonprofits to work for by the Non-Profit Times, our employees enjoy rewarding careers in a fast-paced, results-driven environment. We offer a competitive compensation package, including incentives. Eligible employees also participate in a comprehensive benefits program that includes medical, dental, orthodontia, vision coverage, a student loan repayment program, a 403(b) retirement plan with substantial company match, discounted pet insurance, and generous paid-time-off benefits. The successful applicant will have a hybrid or remote work arrangement, but must reside within a 125-mile radius of the AAA's office located in Downtown Manhattan. The starting salary range for the position is $159,000 - $168,000, plus an annual incentive opportunity targeting up to 20% of the base salary. About the Role: The Director of AI Development applies expertise in AI and Generative AI to design, build, and optimize machine learning systems that enable next-generation solutions at scale. This role focuses on deploying scalable and efficient machine learning models into production, and monitoring and improving the performance of AI systems. The Director collaborates with cross-functional teams to ensure seamless integration of models into business workflows. Responsibilities: + Design and develop end-to-end applications that seamlessly integrate machine learning capabilities, including real-time inference, batch processing, and efficient data management to deliver scalable and robust solutions. + Identify bottlenecks in the model development, deployment, and monitoring process. + Design and implement production-ready machine learning pipelines, including model training, validation, deployment, and monitoring (e.g., labelled data sets to check performance of prompts). + Build scalable, high-performance infrastructure to support Generative AI workflows (e.g., distributed training, inference optimization, and GPU/TPU utilization). + Deploy GenAI applications into production cloud environments with performance, cost, and latency trade-offs considered (e.g., open-source vs. closed-source, quantization, prompt token length, completion caching, prompt caching). + Monitor and troubleshoot model performance, addressing issues such as performance drift and response latency. + Stay at the forefront of Generative AI advancements, identifying opportunities to incorporate the latest research and techniques into production systems. Qualifications: + Bachelor’s or advanced degree in computer science, engineering, or a related field. + 3+ years of experience in machine learning engineering, with a focus on deploying AI systems at scale. + Experience working with large-scale Generative AI applications in production environments. + Relevant experience in the legal domain is a plus. + Strong proficiency in Python and machine learning frameworks (e.g., TensorFlow, PyTorch). + Experience with Generative AI tools and techniques (e.g., LLMs, quantization, synthetic data generation, knowledge distillation, retrieval-augmented generation, fine-tuning). + Proficiency in commons GenAI libraries (e.g., LangChain, Autogen) and cloud-native AI services (e.g., Azure search) + Knowledge of cloud infrastructure (e.g., Azure) and management tools for IT components, storage, networking, and caching. + Familiarity with ML Ops principles, including CI/CD pipelines, containerization, and automated testing for AI systems. + Experience with modern container platforms (e.g., Docker, OpenShift) and tools like Jenkins, Git, and Sonar. + Strong problem-solving skills with the ability to address complex technical challenges. + Excellent communication skills to collaborate with cross-functional teams and explain technical concepts to non-technical stakeholders. + Eagerness to stay updated with cutting-edge AI research and apply innovative ideas to real-world problems. + Organization and attention to detail, ensuring high-quality delivery. + Ability to work collaboratively to create innovative and efficient solutions.