Specialist Solutions Architect - Data Scientist / ML Engineer (Financial Services)

<p data-pm-slice="1 1 []">FEQ427R31</p> <h3><strong>Mission </strong></h3> <p>As a Specialist Solutions Architect (SSA) - Data Scientist / ML Engineer, you will be the trusted technical ML expert to both Databricks customers and the Field Engineering organization. You will work with Solution Architects to guide customers in architecting production-grade ML applications on Databricks, while aligning their technical roadmap with the continually evolving Databricks Data Intelligence Platform. You will continue to strengthen your technical skills through applying cutting edge technologies in GenAI, MLOps, and ML more broadly, expanding your impact through mentorship, and establishing yourself as a ML thought leader.</p> <h3><strong>The impact you will have:</strong></h3> <ul> <li>Architect production level ML workloads for customers using our unified platform, including end-to-end ML pipelines, training/inference optimization, integration with cloud-native services, MLOps, etc. </li> <li>Provide advanced technical support to Solution Architects during the technical sale ranging from feature engineering, training, tracking, serving to model monitoring all within a single platform, as well as participating in the larger ML SME community in Databricks</li> <li>Collaborate cross-functionally with the product and engineering teams to represent the voice of the customer, define priorities and influence the product roadmap, helping with the adoption of Databricks’ ML offerings</li> <li>Build, scale, and optimize customer data science workloads and apply best in class MLOps to productionize these workloads across a variety of domains</li> <li>Serve as the trusted technical advisor for customers developing GenAI solutions, such as RAG architectures on enterprise knowledge repos, querying structured data with natural language, content generation, and monitoring</li> </ul> <h3><strong>What we look for:</strong></h3> <ul> <li>5+ years of hands-on industry ML experience in at least one of the following:</li> <ul> <li>ML Engineer: Build and maintain production-grade cloud (AWS/Azure/GCP) infrastructure that supports the deployment of ML applications, including drift monitoring.</li> <li>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</li> </ul> <li>Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience</li> <li>Experience communicating and/or teaching technical concepts to non-technical and technical audiences alike</li> <li>Passion for collaboration, life-long learning, and driving business value through ML</li> <li>[Preferred] 2+ years customer-facing experience in a pre-sales or post-sales role</li> <li>[Preferred] Experience working with Apache Spark to process large-scale distributed datasets</li> <li>Can meet expectations for technical training and role-specific outcomes within 3 months of hire</li> <li>This role can be remote, but we prefer that you be located in the job listing area and can travel up to 30% when needed.</li> </ul><div class="content-pay-transparency"><div class="pay-input"><div class="description"><p> </p> <p><strong>Pay Range Transparency</strong></p> <p><span style="font-weight: 400; font-size: 14px;">Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation pac ... (truncated, view full listing at source)