Lead ML Engineer - Gen AI

About Lyric

Here at Lyric, we’re building the future of Supply Chain AI, with the team best equipped to build it. Traditional supply chain software, often supplemented with ad hoc analysis, is too rigid for modern AI capabilities. Custom AI solutions are time-consuming, costly to develop in-house, and lack proper supply chain context. These issues make it hard for large supply chain organizations to fully leverage advanced data & decision science.

Our platform, the "Lyric Studio", accelerates your ability to create and consume advanced supply chain models (e.g. stockout prediction & network optimization). A catalog of ready-to-use algorithms and optimization models makes it easy for business users to create sophisticated no-code applications. Lyric Studio also enables technical users to train prediction models & deploy custom algorithms to a private catalog for future reuse. Lyric allows for faster, more advanced supply chain decisions of all shapes and sizes, which is why dozens of Fortune 500 enterprises are saving millions with Lyric today.

Position Overview:

We are seeking a Lead ML Engineer for Gen AI to drive our Generative AI initiatives. This is a strategic and highly technical role responsible for evaluating, designing, and implementing practical Gen AI solutions that will empower our internal teams and external clients. You will focus on building scalable Gen AI systems, such as a Data Copilot for Analysts and Scientists, and play a key role in shaping our Gen AI technology roadmap and architecture.

Key Responsibilities:

1. Model Evaluation and Selection:

   • Evaluate various generative AI models, including LLMs and foundational models, based on performance, scalability, and alignment with business needs.

   • Conduct experiments to benchmark model performance, accuracy, and reliability in real-world applications.

2. System Architecture and Design:

   • Architect Gen AI systems, ensuring they are scalable, secure, and capable of handling complex, data-intensive tasks.

   • Design and implement solutions such as Data Copilots for Analysts/Scientists to enhance data-driven insights and decision-making capabilities.

3. Gen AI Development and Integration:

   • Lead the development of AI-driven applications and integrate them into Lyric's existing platforms and tools.

   • Work closely with data science, engineering, and product teams to build and deploy AI solutions that are both innovative and practical.

4. Technology Roadmap and Strategy:

   • Define and implement a technical roadmap for Gen AI at Lyric, keeping pace with industry trends and technological advancements.

   • Make informed technology and infrastructure choices to support Lyric’s long-term Gen AI goals, from model deployment to scalability strategies.

5. Team Collaboration and Mentorship:

   • Collaborate with cross-functional teams, including data scientists, engineers, and product managers, to translate complex requirements into actionable Gen AI solutions.

   • Mentor and guide junior engineers and AI practitioners on best practices in generative AI development and deployment.

6. Industry Research and Thought Leadership:

   • Stay up-to-date with the latest developments in Gen AI, and provide insights on emerging tools, techniques, and best practices.

   • Contribute to Lyric's thought leadership in Gen AI through publications, presentations, and participation in industry events.

Qualifications:

• Experience:

  • 7+ years in AI/ML roles, with at least 3 years focused on Gen AI or NLP model evaluation and implementation.

  • Proven experience as an AI architect or technical lead in deploying scalable AI solutions.

• Technical Skills:

  • Deep knowledge of generative AI models (LLMs, GANs, VAEs, etc.) and model architectures.

  • Proficiency in Python, TensorFlow, PyTorch, and other AI/ML frameworks.

  • Hands-on experience with model tuning, fine-tuning, prompt engineering, and performance optimization.

• Domain Knowledge:

  • Strong understanding of data analytics, BI, and data-driven decision-making processes.

  • Familiarity with MLOps practices and model lifecycle management in production environments.