Specialist Solutions Architect - Ai/ml

Databricks Databricks · Data AI · NJ · Field Engineering - FE Direct Regulated

This role is for a Specialist Solutions Architect focused on AI/ML Engineering at Databricks. The individual will act as a technical expert for customers and internal teams, guiding them in architecting and deploying production-grade ML and AI applications on the Databricks platform. Key responsibilities include architecting AI workloads, serving as a practitioner for GenAI solutions (RAG, agents, evaluation), building and optimizing AI workloads with MLOps, providing advanced technical support, and influencing the product roadmap. The role requires 5+ years of hands-on ML experience and strong communication skills.

What you'd actually do

  1. 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.
  2. 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
  3. Build, scale, and optimize customer AI workloads and apply best-in-class MLOps to productionize these workloads across a variety of domains
  4. 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
  5. 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

Skills

Required

  • 5+ years of hands-on industry ML experience
  • Experience communicating and/or teaching technical concepts to non-technical and technical audiences alike

Nice to have

  • Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience
  • 2+ years customer-facing experience in a pre-sales or post-sales role

What the JD emphasized

  • production-grade ML & AI applications
  • enterprise GenAI solutions
  • productionize these workloads
  • technical sale
  • voice of the customer

Other signals

  • customer-facing technical expert
  • architecting production-grade ML & AI applications
  • GenAI solutions
  • MLOps