✨ About The Role
- As a Senior ML Engineer on the Sales AI team, you will be responsible for designing and building AI automation and augmentation for the new Sales product line.
- Your role will involve integrating AI and machine learning technologies into the sales platform to enhance the user experience.
- You will work closely with product managers and other engineers to deliver a high-quality, world-class product.
- You will lead group sessions and participate in the product to engineering project lifecycle to ensure the team is guided by product-focused architecture.
- You will guide your team in making optimal technical and infrastructure decisions and use POCs and new technologies to drive change.
- You will mentor other engineers on best practices and learning opportunities in AI/ML.
- You will contribute to roadmap ideation and lead planning and project management for AI initiatives within the Sales product.
- You will be accountable for defining scope, estimating tasks, and managing risks and changes in AI projects.
- You will help build a strong team by providing candid, constructive feedback to improve team processes and outcomes.
âš¡ Requirements
- You should be an effective communicator capable of explaining complex technical ideas to non-technical teams.
- You have experience in building products from the ground up and understand how different parts of a system work together.
- You are skilled in driving experimentation in new domains and can make informed decisions about technical strategies.
- You have experience in building and deploying LLM-driven applications, with a strong grasp of the underlying principles and performance evaluation.
- You are familiar with AI Ops and tooling specific to LLMs, such as vector databases and frameworks like PyTorch.
- You possess intellectual curiosity and stay updated with the latest research in the AI field.
- You are experienced in the end-to-end machine learning model lifecycle, including prototyping, implementation, deployment, and monitoring using AWS tools.
- You advocate for engineering best practices in machine learning, including code quality, testing, monitoring, and model evaluation.