Senior Specialist Solutions Engineer (AI/ML)

The shape of it

In the posting’s own words

As a Senior Specialist Solutions Engineer (SSE), ML Engineering, 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.

What it asks for · 13

• Experienced, technical, customer-facing, and with a background in Data Science / Machine Learning, and Data Engineering. Looking to learn and develop in a customer-facing technical role as a subject matter expert (SME) in a pre-sales environment.
• Pre-sales or post-sales experience working with external clients across a variety of industry markets
• Hands-on industry ML experience in at least one of the following:
• ML Engineer: Develop production-grade cloud (AWS/Azure/GCP) infrastructure that supports the deployment of ML applications, including drift monitoring
• Data Scientist: Experience with the latest techniques in natural language processing, including vector databases, fine-tuning LLMs, and deploying LLMs with tools such as HuggingFace, Langchain, and OpenAI
• Hands-on experience working with 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] 2+ years customer-facing experience in a pre-sales or post-sales role
• [Preferred] Experience working with Apache Spark™ to process large-scale distributed datasets
• Can meet expectations for technical training and role-specific outcomes within 3 months of hire
• Can travel up to 30% when needed

What the job covers

• Lead the architectural design of production-grade ML workloads on our unified platform, encompassing the entire MLOps lifecycle from end-to-end pipeline creation and optimization (training/inference) to seamless integration with cloud-native services.
• Provide 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.
• Serve as the trusted technical advisor for customers developing GenAI solutions, specializing 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.
• Drive 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.

Degree language

• Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience

Tools and skills named

Words the posting leans on

• technical13×
• experience8×
• data7×
• engineering5×
• solutions5×
• customers4×
• science4×
• customer-facing3×
• expert3×
• genai3×
• industry3×
• llms3×
• platform3×
• production-grade3×
• skills3×
• aligning2×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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