Specialist Solutions Architect - Ai/ml

Databricks Databricks · Data AI · Bangalore, India · Field Engineering - Other

Databricks is seeking an AI/ML Specialist Solutions Architect in Bengaluru, India, to guide customers in architecting production-grade ML & AI applications on their Data Intelligence Platform. The role involves advising on GenAI solutions, MLOps, agents, RAG, and optimizing AI workloads, while collaborating with product and engineering teams.

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-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
  • architecting production-grade ML & AI applications
  • GenAI
  • MLOps
  • agents
  • RAG architectures
  • agentic systems (tool-calling agents, multi-agent orchestration, guardrails)
  • natural language querying of structured data
  • AI evaluation and observability
  • monitoring systems
  • training/inference optimization
  • integration with cloud-native services
  • feature engineering
  • model monitoring
  • technical concepts to non-technical and technical audiences

Nice to have

  • Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience
  • customer-facing experience in a pre-sales or post-sales role
  • travel up to 30%

What the JD emphasized

  • 5+ years of hands-on industry ML experience
  • production-grade ML & AI applications
  • enterprise GenAI solutions
  • productionize these workloads

Other signals

  • Architect production-grade ML & AI applications
  • trusted technical ML & AI expert
  • customer technical roadmap
  • GenAI
  • MLOps