Description
As a Specialist Solutions Architect - AI & ML Engineer, you will be the trusted technical ML & AI expert to both Databricks customers and the Field Engineering organization.
You will work with Solution Architects to guide customers in architecting production-grade ML & AI applications on Databricks, while aligning their technical roadmap with the continually evolving Databricks Data Intelligence Platform.
Your responsibilities will include:
- Architecting 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.
- Serving 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
- Building, scaling, and optimizing customer AI workloads and applying best-in-class MLOps to productionize these workloads across a variety of domains
- Providing 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
- Collaborating 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
We are looking for someone with 5+ years of hands-on industry ML experience in at least one of the following areas:
- 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
A graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience is also required.
Additionally, experience communicating and/or teaching technical concepts to non-technical and technical audiences alike is highly valued.
The salary range for this position is $180,000-$247,500 USD, depending on location.