Staff ML/AI Platform Engineer (Databricks)
Staff engineer who designs, builds, and operates Curinos' Databricks-native AI/ML platform — creating CI/CD, infrastructure-as-code, observability, and governance for production ML models, LLM apps, and agentic workflows at a fintech. Core stack: Databricks (Workflows, Unity Catalog, MLflow, Delta), Python/PySpark.
- Compensation
- $160K–$170K / yr
- Location
- New York, NY, US
- Work setup
- Not specified
- Seniority
- Staff
- Employment
- Part time
- Experience
- Not specified
- Category
- Ml / AI
- Posted
Skills
Must have
- Strong experience building and operating ML, AI, data, or cloud platforms in production
- Deep hands-on expertise with Databricks: Workflows, Delta Lake, Delta Live Tables, Unity Catalog, Asset Bundles, MLflow, Python, PySpark, SparkSQL
- Experience with modern MLOps: CI/CD, automated testing, model deployment, feature management, observability, governance
- Experience with LLMs, agentic AI systems, evaluation frameworks, AI safety controls, model monitoring
- Experience with model serving architectures, APIs, MCP servers, scalable inference systems
- Understanding of production support, incident management, operational excellence
- Strong communication and cross-disciplinary collaboration skills
- Ability to guide technical architecture, establish engineering best practices, mentor engineers
Full 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.
Curinos operates under a hybrid modality and has office locations in New York, Chicago, Boston, Toronto, and London. This role is open to hybrid/ remote candidates based in the United States.
Job Description
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.