Specialist Solutions Architect- AI/ML

Details

• Architect production-level ML & AI workloads for customers using our unified platform, including agents, end-to-end ML pipelines, training/inference optimization, integration with cloud-native services, MLOps, etc.
• Serve as a trusted practitioner for enterprise GenAI solutions, including RAG architectures, agentic systems (tool-calling agents, multi-agent orchestration, guardrails), natural language querying of structured data, AI evaluation and observability, and monitoring systems
• Build, scale, and optimize customer AI workloads and apply best-in-class MLOps to productionize these workloads across a variety of domains
• 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
• 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’ AI offerings
• 5+ years of hands-on industry ML experience in at least one of the following:
• ML Engineer: Build and maintain production-grade cloud (AWS/Azure/GCP) infrastructure that supports the deployment of ML applications, including drift monitoring.
• AI Engineer: Experience with the latest techniques in LLMs & agentic systems, including vector databases, fine-tuning LLMs, AI guardrail systems, and deploying LLMs with tools such as HuggingFace, Langchain, and OpenAI
• Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience
• Experience communicating and/or teaching technical concepts to non-technical and technical audiences alike
• Passion for collaboration, life-long learning, and driving business value through ML & AI
• [Preferred] 2+ years customer-facing experience in a pre-sales or post-sales role
• Can meet expectations for technical training and role-specific outcomes within 3 months of hire
• Can travel up to 30% when needed
• ML Engineer: Build and maintain production-grade cloud (AWS/Azure/GCP) infrastructure that supports the deployment of ML applications, including drift monitoring.
• AI Engineer: Experience with the latest techniques in LLMs & agentic systems, including vector databases, fine-tuning LLMs, AI guardrail systems, and deploying LLMs with tools such as HuggingFace, Langchain, and OpenAI