Senior Specialist Solutions Engineer (AI/ML)

Databricks; Greater London; On-site; GBP 75,000 - 95,000; Full time

Job summary

Databricks is looking for a Senior Specialist Solutions Engineer (SSE) specializing in Machine Learning to guide customers in architecting production-grade ML applications. This role demands a strong technical background and customer-facing experience.

You will be involved in designing and implementing solutions on the Databricks platform, providing technical support, and driving community adoption through thought leadership. A graduate degree in a quantitative field is required, along with hands-on ML experience.

Qualifications

• Experienced technical customer-facing expert in Data Science / Machine Learning.
• Pre-sales or post-sales experience working with external clients.
• Hands-on industry ML experience in building production-grade applications.

Responsibilities

• Lead architectural design of production-grade ML workloads.
• Provide advanced technical support during technical sales cycle.
• Serve as trusted technical advisor for GenAI solutions.
• Drive community growth and platform adoption through thought leadership.

Skills

Machine Learning

Data Engineering

Natural Language Processing

Distributed Spark

Education

Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics)

Tools

AWS

Azure

GCP

HuggingFace

Langchain

OpenAI

Job description

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.

You will be reporting to the Manager, Field Engineering (Specialist Team).

The impact you will have:

• 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.

What we look for:

• 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.

Data Science/ML Skills:

• 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)