> Markdown version of [/jobs/ext/3973157-staff-ml-ai-platform-engineer-databricks](https://www.wearedevelopers.com/jobs/ext/3973157-staff-ml-ai-platform-engineer-databricks). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Staff ML/AI Platform Engineer (Databricks)

- **Company:** Curinos Inc
- **Location:** New York, NY, United States (Remote available)
- **Salary:** $160,000.0 - $170,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Audit Trail, Automation of Tests, Continuous Integration, Python (Programming Language), Machine Learning, Reliability Engineering, Software Safety, Large Language Models, Generative AI, Agentic-AI, Data Lakes, AI Platforms, Pyspark, Machine Learning Operations, Databricks
- **Published:** October 27, 2026
- **Apply:** https://www.thejobnetwork.com/job/8dfd0463-7d2d-4011-9aea-864a3cf40e2c/staff-mlai-platform-engineer-databricks

## About the Role

* Strong experience building and operating ML, AI, data, or cloud platforms in production environments
 * Deep hands-on expertise with Databricks, including Workflows, Delta Lake, Delta Live Tables, Unity Catalog, Asset Bundles,MLflow, Python,PySpark, andSparkSQL
 * Experience with modernMLOpspractices including CI/CD, automated testing, model deployment, feature management, observability, and governance
 * Experience working with LLMs, agentic AI systems, evaluation frameworks, AI safety controls, and model monitoring solutions
 * Experience with model serving architectures, APIs, MCP servers, and scalable inference systems
 * Understanding of production support, incident management, and operational excellence practices
 * Strong communication skills and the ability to work effectively across multidisciplinary teams that make use of the AI and ML workflows you build.
 * Demonstrated ability to guide technical architecture, establish engineering best practices, and mentor engineers across teams

## Description

Curinos empowers financial institutions to put customers at the center of every decision. Our AI-first platform transforms proprietary data, advanced analytics and deep financial services expertise into timely recommendations - delivered right where teams work. The result: confident decisions, stronger customer relationships, and lasting, profitable growth., We are seeking a Staff ML/AI Platform Engineer to help build and operate Curinos' Databricks-native AI platform. This role sits at the intersection of machine learning engineering, platform engineering, and site reliability, with a focus on enabling reliable deployment and operation of AI and ML systems at scale. This is a high-impact role that will unlock the next phase of Curinos scalable growth.

You will be responsible for designing and implementing the infrastructure, tooling, automation, observability, and governance capabilities that power production machine learning models, LLM applications, and agentic AI workflows. You will help establish technical architecture and engineering standards across teams, and work with applied scientists to move solutions from experimentation to production quickly, safely, and repeatably. You will "build it once and scale it many times."

We are a Databricks-first organization and make extensive use of Databricks Workflows, Asset Bundles, Unity Catalog, MLflow, Delta Live Tables, and related ecosystem tools. You will work closely with data scientists, data engineers, software engineers, and product teams embed AI and ML capabilities into our products, as well as automate operational processes to achieve reliable, scalable efficiencies across data and algorithmic workflows company-wide.

As a FinTech company operating in a regulated environment, we place strong emphasis on governance, reproducibility, observability, and operational excellence. This role is critical to ensuring our AI platform and ML solutions meet those standards while remaining fast-moving and developer-friendly.

Responsibilities

 * Design, build, and maintain platform capabilities for deploying, monitoring, and operating ML models, LLM applications, and agentic AI workflows.providing technical direction on architecture and scalability
 * Develop automated CI/CD pipelines and infrastructure-as-code patterns for AI and machine learning workloads
 * Create platformtoolingthatstandardizes deployment practicesof AI and ML capabilitiesacross teams.establishing reusable engineering patterns and best practices
 * Build observability solutions that track model health, service reliability, cost, performance, AI safety metrics, and business outcomes.
 * Implement scalable evaluation and monitoring frameworks for LLMs, agentic workflows, and generative AI applications.
 * Partner withscientists and engineers toproductionizenewMLmodels and AI capabilities.
 * Support governance, auditability, reproducibility, and model lifecycle management requirements.
 * Strongly advocate and demonstrateoperational excellence practices includingplatform automation,reliability improvements,incident response, root cause analysis
 * Design, revise, and document architecture patterns, operational procedures, and engineering best practices guiding technical decisions and implementation approaches providing technical guidance and mentorship to other engineers