# Senior Specialist Solutions Architect

**Company:** [Databricks](https://hotfix.jobs/companies/databricks)
**Location:** Melbourne, Australia, Sydney, Australia
**Role:** Solutions Architecture
**Experience:** 5+ years
**Skills:** Machine Learning, Generative AI, MLOps, AWS, Microsoft Azure, GCP, LLMs, RAG, AI Agents, Vector Databases, Hugging Face, LangChain, OpenAI, Model Monitoring, Drift Monitoring
**Posted:** 2026-08-12

> Specialist Solutions Architect who guides customers in designing and productionizing ML and AI workloads, including generative AI, agentic systems, and MLOps. Requires 5+ years of hands-on ML experience, advanced quantitative education or equivalent experience, and strong technical communication skills.

## Job Description

## Responsibilities
- Architect production-level ML and AI workloads for customers using a unified platform, including agents, end-to-end ML pipelines, training and inference optimization, cloud-native service integrations, and MLOps.
- Serve as a trusted practitioner for enterprise generative AI solutions, including retrieval-augmented generation (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 them across varied domains.
- Provide advanced technical support to Solution Architects during technical sales, covering feature engineering, training, tracking, serving, and model monitoring.
- Participate in the broader ML subject-matter-expert community.
- Collaborate with product and engineering teams to represent customer needs, define priorities, influence the product roadmap, and drive adoption of AI offerings.

## Requirements
- 5+ years of hands-on industry machine learning experience in at least one of the following areas:
  - **ML engineering:** Building and maintaining production-grade cloud infrastructure on AWS, Azure, or GCP for deploying ML applications, including drift monitoring.
  - **AI engineering:** Applying current LLM and agentic-system techniques, including vector databases, LLM fine-tuning, AI guardrail systems, and deploying LLMs with Hugging Face, LangChain, and OpenAI.
- Graduate degree in a quantitative discipline such as Computer Science, Engineering, Statistics, or Operations Research, or equivalent practical experience.
- Ability to communicate and teach technical concepts to both technical and non-technical audiences.
- Passion for collaboration, continuous learning, and driving business value through ML and AI.
- Ability to meet technical training and role-specific expectations within three months of hire.
- Ability to travel up to 30% as needed.

## Nice-to-haves
- 2+ years of customer-facing experience in a pre-sales or post-sales role.

## Benefits
- Comprehensive benefits and perks are offered, with details varying by region.

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