Senior Specialist Solutions Architect (ai/ml)

Databricks Databricks · Data AI · London, United Kingdom · Field Engineering - Other

Senior Specialist Solutions Architect (AI/ML) at Databricks, focusing on guiding enterprise customers in architecting and implementing production-grade ML and AI applications on the Databricks platform. The role involves deep expertise in GenAI solutions, including RAG, agentic systems, AI observability, and natural language querying, as well as architecting end-to-end pipelines and MLOps lifecycle management. It also includes pre-sales technical support, product influence, and thought leadership through content creation and presentations.

What you'd actually do

  1. Design and implement production-level ML and AI workloads, including end-to-end pipelines, training/inference optimization, MLOps lifecycle management, and integration with cloud-native services.
  2. Serve as a practitioner for enterprise GenAI solutions, specializing in RAG architectures, agentic systems (including tool-calling, multi-agent orchestration, and guardrails), AI observability, and natural language querying of structured data.
  3. Provide advanced technical support to Solution Architects during the technical sales cycle by building MVPs, leading deep-dive sessions, and aligning AI solutions with complex customer business challenges.
  4. Collaborate cross-functionally with product and engineering teams to represent the voice of the customer, define priorities, and influence the platform’s AI roadmap.
  5. Drive community growth and AI platform adoption through the creation of technical tutorials and training materials, as well as by presenting at industry conferences and leading hackathons.

Skills

Required

  • 10+ years of hands-on industry DS/ML experience
  • Hands-on experience working with Distributed Spark based systems
  • Experience with data engineering concepts or a good understanding of data engineering concepts
  • Pre-sales or post-sales experience working with external clients
  • Proven ability to communicate and teach complex technical concepts to both technical and non-technical audiences.

Nice to have

  • Minimum of 5+ years of customer-facing experience
  • Experience working with Apache Spark™ to process large-scale distributed datasets
  • Graduate degree in a quantitative discipline (e.g., Computer Science, Engineering, Statistics, Operations Research, etc) or equivalent practical experience.

What the JD emphasized

  • production-grade ML and AI applications
  • enterprise GenAI solutions
  • production-level ML and AI workloads
  • training/inference optimization
  • MLOps lifecycle management
  • agentic systems
  • tool-calling
  • multi-agent orchestration
  • guardrails
  • AI observability
  • natural language querying
  • technical sales cycle
  • voice of the customer
  • AI roadmap

Other signals

  • Architecting production-grade ML and AI applications
  • Serve as a practitioner for enterprise GenAI solutions
  • Design and implement production-level ML and AI workloads