About this role.
Databricks is seeking a customer-facing AI/ML Specialist Solutions Architect to help enterprises design and productionize machine-learning and generative-AI workloads on its data and AI platform. The role combines deep hands-on expertise in MLOps, ML infrastructure, LLM applications, RAG, agentic systems, evaluation, and model observability with technical pre- and post-sales partnership. The architect will guide solution design, optimize training and inference, mentor others, and communicate customer needs to product and engineering teams. Candidates need at least five years of industry ML experience, strong cloud-platform knowledge, and the ability to explain technical concepts to diverse audiences. The position is based in São Paulo, Brazil, can be remote, and may require up to 30% travel.
Role DNA
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Job Complexity
5/5Pace & Pressure
4/5Autonomy Level
4/5Communication Load
5/5Salary analysis
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Core skills
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Sample interview questions
I would begin with the business use case, data sensitivity, latency targets, and evaluation criteria. I would design governed ingestion and chunking pipelines, embeddings and vector retrieval, an LLM orchestration layer with citations and guardrails, and offline and online evaluation for relevance, groundedness, safety, latency, and cost. I would also implement monitoring, access controls, feedback capture, and an iterative process for improving retrieval and prompts.
I would establish baseline distributions and performance metrics during validation, then monitor feature drift, prediction drift, data-quality checks, and delayed ground-truth performance where available. Alert thresholds should be linked to business impact rather than statistical movement alone. When drift is detected, I would investigate upstream data changes, segment performance, retrain or recalibrate when justified, validate the replacement model, and deploy it through a controlled rollout.
I frame MLOps as the operating model that makes AI reliable, repeatable, governed, and measurable after a proof of concept. It reduces the risk of models failing silently, shortens the path from experimentation to business value, and provides visibility into cost, quality, compliance, and performance. I connect the practices directly to outcomes such as faster deployment, lower operational risk, and sustained model value.
I would first instrument the workflow to identify the primary cost and latency drivers, including prompt size, retrieval quality, model selection, token volume, concurrency, and external-tool calls. Potential improvements include better retrieval and reranking, prompt compression, response caching, batching, smaller models for simpler tasks, and routing requests by complexity. I would validate changes against quality and safety metrics so that performance gains do not degrade the user experience.
I would clarify the customer problem, expected impact, urgency, and whether the request represents a repeatable market need or a one-off customization. I would document technical requirements and evidence, bring a structured recommendation to product and engineering partners, and communicate transparently with the customer about viable alternatives and timelines. My goal would be to preserve customer trust while advocating for scalable platform capabilities rather than unsustainable exceptions.
P-226
As an AI/ML Specialist Solutions Architect (SSA), 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. You will continue to strengthen your technical skills through applying cutting-edge technologies in GenAI, MLOps, and ML more broadly, expanding your impact through mentorship, and establishing yourself as an AI thought leader. This role can be remote.
The impact you will have:
- 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.
- 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
- Build, scale, and optimize customer AI workloads and apply best-in-class MLOps to productionize these workloads across a variety of domains
- 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
- 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
What we look for:
- 5+ years of hands-on industry ML experience in at least one of the following:
- 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
- Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience
- Experience communicating and/or teaching technical concepts to non-technical and technical audiences alike
- Passion for collaboration, life-long learning, and driving business value through ML & AI
- [Preferred] 2+ years customer-facing experience in a pre-sales or post-sales role
- Can meet expectations for technical training and role-specific outcomes within 3 months of hire
- Can travel up to 30% when needed
About Databricks
Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn, X, YouTube, and Instagram.
Benefits
At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here.
Our Commitment to Diversity and Inclusion
At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.
Compliance
If access to export-controlled technology or source code is required for performance of job duties, it is within Employer’s discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.
Annual salary information is not provided for this position. Explore salary ranges for similar roles in our Salary Directory ›
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