高级 AI/ML 运维工程师
Senior AI/ML Operations Engineer
**关于我们**
Abacus Insights 正在改变健康计划如何使用数据。我们的使命很简单：让医疗数据变得可用，使负责护理和成本决策的人能够更快、更有信心地采取行动。
我们帮助健康计划打破数据孤岛，创建一个单一、可信的数据基础。这个基础推动更好的决策——使计划能够改善结果、减少浪费，并为成员和提供者带来更好的体验。我们获得了来自顶级投资者的 1000 万美元投资，正在解决一个亟需变革的行业中的重大挑战。我们的平台通过提供干净、连接且可靠医疗数据来支持自动化、优先级排序和决策工作流，实现 GenAI 应用场景，这也是我们走在前列的原因。
我们的创新始于人才。我们大胆、好奇、协作——因为最好的想法来自于共同合作。我们注重 AI 和自动化的合理使用，以推动创新和效率，我们寻找那些充满好奇心和适应力强的人——那些热衷于利用新兴技术来提升工作方式的人——同时将人类洞察力、联系和客户置于每个决策的核心。
准备好产生影响吗？加入我们，一起共建未来。
**职位描述**
高级 AI/ML 运维工程师是负责基础设施、管道和运营可靠性的高级个人贡献者，这些支撑了 Databricks 和 Snowflake 上的经典机器学习（ML）和生成式 AI（GenAI）/代理系统。该职位结合了平台和管道工程、经典 ML 运维以及 GenAI/代理系统基础设施，具备人工智能（AI）和/或 ML 的深入实践经验。
你将负责生产环境中 ML 和 GenAI 系统的可靠性，从 ML/AI 特定的管道和部署流程到模型生命周期管理，再到检索和代理工具的基础架构，将 AI 工程和数据科学工作从原型转化为可靠的生产系统。该职位是 AI 工程和代理工作的主要运营支持，与平台和基础设施团队协调处理环境配置、访问设置和底层 CI/CD 工具等方面的工作。
**日常职责**
**平台与管道工程**
- 使用由 Infra/DevOps 维护的 CI/CD 基础设施，在不同环境中部署和推广 ML 和 GenAI 模型、管道和代码
- 开发并推广可重用的 de
查看英文原文
**About Us**
Abacus Insights is transforming how data works for health plans. Our mission is simple: make healthcare data usable, so the people responsible for care and cost decisions can act faster, with confidence.
We help health plans break down data silos to create a single, trusted data foundation. That foundation powers better decisions—so plans can improve outcomes, reduce waste, and deliver better experiences for members and providers alike. Backed by $100M from top investors, we’re tackling big challenges in an industry that’s ready for change. Our platform enables GenAI use cases by delivering clean, connected, and reliable healthcare data to support automation, prioritization, and decision workflows—and it’s why we are leading the way.
Our innovation begins with people. We are bold, curious, and collaborative—because the best ideas come from working together. We embrace the thoughtful use of AI and automation to drive innovation and efficiency, and we look for individuals who are curious and adaptable—those excited to leverage emerging technologies to enhance how we work—while keeping human insight, connection, and our clients at the center of every decision.
Ready to make an impact? Join us and let’s build the future together.
**About the Role**
The Senior AI/ML Ops Engineer is a senior individual contributor responsible for the infrastructure, pipelines, and operational reliability that power both classical machine learning (ML) and generative AI (GenAI)/agentic systems on Databricks and Snowflake. This role blends platform and pipeline engineering, classical ML operations, and GenAI/agentic systems infrastructure, with hands-on depth in artificial intelligence (AI) and/or ML.
You will own the reliability of ML and GenAI systems in production, from ML/AI-specific pipeline and deployment workflows through model lifecycle management to the infrastructure behind retrieval and agentic tooling, taking AI engineering and data science work from prototype to reliable production systems. This role serves as the primary operational support for AI Engineering and agentic work, and coordinates with platform and infrastructure teams on things like environment provisioning, access configuration, and underlying CI/CD tooling.
**Your day to day**
**Platform & Pipeline Engineering**
- Deploy and promote ML and GenAI models, pipelines, and code across environments, using CI/CD infrastructure maintained by Infra/DevOps
- Develop and promote reusable deployment patterns and tooling to reduce the effort required to stand up new AI/ML use cases and client-specific deployments
- Build and maintain data pipelines supporting both classical ML and GenAI workloads, from ingestion through feature engineering to serving
- Operate within Databricks and Snowflake governance frameworks (e.g. Unity Catalog access controls, environment boundaries) to ensure secure, compliant promotion of code, data, and models, and support governance practices/tooling more broadly as the team's AI/ML footprint grows
- Independently diagnose and resolve production issues across pipelines, infrastructure, and model-serving systems
**Classical ML Operations**
- Automate and monitor production ML inference and feature engineering workflows, including alerting and incident response
- Own model lifecycle management using a model registry tool such as MLflow, along with Unity Catalog: experiment tracking, model registration, versioning, and controlled promotion across environments
**GenAI & Agentic Infrastructure**
- Build and maintain infrastructure for retrieval-augmented generation (RAG) systems, including vector search indexing and retrieval pipelines
- Deploy, host, and maintain MCP servers and tool integrations for agentic applications
- Build and maintain evaluation infrastructure for AI systems, and contribute to evaluation methodology in partnership with AI engineering
- Support agent observability: logging, tracing, and monitoring for agent and model behavior in production
**Cross-Functional**
- Partner with business stakeholders to scope data and feature requirements
- Coordinate with Software Engineering, Data Engineering, Data Science, security, and DevOps on infrastructure changes and shared platform needs
- Mentor junior engineers on platform practices and operational standards
- Occasionally contribute to customer-specific implementation work as part of a broader team (e.g. semantic layer configuration, domain-specific analytics builds such as Rx trend reporting)
**What you bring to the team**
- 5+ years of experience in AI/ML engineering, MLOps, or a closely related discipline
- Extensive hands-on experience in AI/ML, with meaningful depth in at least one of the following, and some working exposure to the other:
- **AI/GenAI:** agentic frameworks (e.g. LangChain), RAG systems, vector search, MCP or comparable tool-integration protocols, model serving/gateway layers, evaluation design
- **Classical ML:** model development across common algorithm families (e.g. XGBoost, gradient boosting, random forest, neural networks), feature engineering, training pipelines, production deployment
- Deep, hands-on experience with Databricks and/or Snowflake, including ML/AI pipeline development, working within governance/access-control frameworks (e.g. Unity Catalog), and integrating with existing CI/CD infrastructure
- Strong hands-on experience with a model lifecycle/registry tool such as MLflow: experiment tracking, model registration, versioning, and promotion across environments
- Strong proficiency in Python and SQL
- Demonstrated ability to independently diagnose and resolve production infrastructure issues
- Excellent communication skills and comfort working directly with non-technical stakeholders
- A track record of proactively learning new tools and frameworks and applying them quickly to real work
**What we would like to see, but not required**
- Experience in healthcare, health insurance, or regulated data environments
- Experience building or operating multi-agent systems
- Experience with Mosaic AI Gateway or comparable model-serving/gateway platforms
**Compensation**
Compensation for this role is based on experience, skills, and location, and includes base salary plus eligibility for performance bonuses and equity grants.
**What you'll get in return**
- Unlimited paid time off — recharge when you need it.
- Work from anywhere — flexibility to fit your life.
- Comprehensive health coverage — multiple plan options to choose from.
- Equity for every employee — share in our success.
- Growth-focused environment — your development matters here.
- Home office setup allowance — one-time support to get you started.
- Monthly cell phone allowance — stay connected with ease.
**Our Commitment as an Equal Opportunity Employer**
As a mission-led technology company helping to drive better healthcare outcomes, Abacus Insights believes that the best innovation and value we can bring to our customers comes from diverse ideas, thoughts, experiences, and perspectives. Therefore, we dedicate resources to building diverse teams and providing equal employment opportunities to all applicants. Abacus prohibits discrimination and harassment regarding race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.
At the heart of who we are is a commitment to continuously and intentionally building an inclusive culture—one that empowers every team member across the globe to do their best work and bring their authentic selves. We carry that same commitment into our hiring process, aiming to create an interview experience where you feel comfortable and confident showcasing your strengths. If there’s anything we can do to support that—big or small—please let us know.
**AI Use in Recruitment**
We use AI-powered tools throughout our recruitment process. This includes Greenhouse's Real Talent Matching Technology, which helps prioritize applications based on job-related criteria, as well as additional AI tools used to support sourcing, interview preparation, and other recruiting tasks. **We do not remove the human element—our recruiters and hiring teams review all applications and make all decisions related to candidate progression and hiring, regardless of which tools are used to support that process.**
By applying, you acknowledge that we will collect and process your personal information for recruiting purposes in accordance with applicable data protection laws. Please review our [Applicant Privacy Notice](https://abacusinsights.com/applicant-privacy-policy/) for more information, including your rights.