Description
As a Senior Specialist Solutions Engineer (SSE), you will be the trusted technical ML expert to both Databricks customers and the Field Engineering organisation. You will work with Solution Architects to guide customers in architecting production-grade ML applications on Databricks, while aligning their technical roadmap with the evolving Databricks Data Intelligence Platform.
You will continue to strengthen your technical skills through applying the latest technologies in GenAI, LLMOps, and ML, while expanding your impact through mentorship and establishing yourself as an ML expert.
Key responsibilities include:
- Leading the architectural design of production-grade ML workloads on our unified platform, encompassing the entire MLOps lifecycle from end-to-end pipeline creation and optimisation (training/inference) to seamless integration with cloud-native services.
- Providing advanced technical support to the Solution Architects during the technical sales cycle by building MVPs, leading deep-dive technical sessions, and strategically aligning ML/data science solutions to complex customer business challenges using relevant real-world examples.
- Serving as the trusted technical advisor for customers developing GenAI solutions, specialising in the design and implementation of RAG architectures on enterprise knowledge bases, enabling natural language querying of structured data, and establishing content generation and monitoring frameworks.
- Driving community growth and platform adoption through thought leadership activities, including the creation of technical tutorials and training materials, as well as leading hackathons and presenting at industry conferences.
We are looking for an experienced, technical, customer-facing individual with a background in Data Science / Machine Learning, and Data Engineering. You should be looking to learn and develop in a customer-facing technical role as a subject matter expert (SME) in a pre-sales environment.
Required skills include:
- Hands-on industry ML experience in at least one of the following: ML Engineer, Data Scientist, or Distributed Spark based systems.
- Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience.
- Experience communicating and teaching technical concepts to non-technical and technical audiences alike.
- Passion for collaboration, life-long learning, and driving our values through ML.
Preferred skills include:
- 2+ years customer-facing experience in a pre-sales or post-sales role.
- Experience working with Apache Spark to process large-scale distributed datasets.