Director of AI Development

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 s 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 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., Tensor Flow, 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., Lang Chain, 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, Open Shift) 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.