Principal Data & AI Platform Engineer, DataOps

Teladoc Health•Uniondale, NY

1d•$200,000 - $230,000•Remote

About The Position

As a Principal Data & AI Platform Engineer, you will architect, build, and operationalize AI-augmented capabilities that make our data platform more resilient, observable, scalable, and intelligent. This role focuses on DataOps - the systems that operate and support data pipelines rather than the pipelines themselves - including AI-driven monitoring, failure detection and recovery, data quality, and operational decisioning. The ideal candidate combines deep data and software engineering experience with hands-on experience productionizing AI and agentic capabilities in engineering workflows. You will set technical direction, build solutions, lead cross-functional architecture and implementation efforts, and mentor engineers across the organization.

Requirements

Bachelor's degree in Computer Science, Engineering, or a related field; equivalent work experience is acceptable.
10+ years of experience across data engineering, software engineering, or platform engineering, including senior-level architecture and large-scale system design.
3+ years of hands-on experience applying AI, GenAI, or agentic capabilities to data engineering, platform, or operational workflows.
Proven experience architecting or engineering large-scale data processing systems serving a broad population of users or downstream consumers.
Hands-on experience productionizing AI-augmented or agentic solutions, including evaluation, monitoring, failure handling, and operational support.
Strong experience with modern data platforms such as Databricks or Snowflake and large-scale/distributed data processing patterns.
Demonstrated ability to lead technical design and execution across teams, communicate with technical and business stakeholders, and influence without direct authority.

Nice To Haves

Experience with agentic AI and engineering frameworks such as Databricks Agent Bricks, LangChain, Hugging Face, or similar technologies.
Experience with context layers, semantic layers, model evaluation, or other patterns that make enterprise data more usable by AI systems.
Deep experience with data quality, data observability, lineage, fault tolerance, or automated remediation at scale.
Experience in healthcare or another regulated industry.

Responsibilities

Architect and implement AI and agentic capabilities that improve the reliability, scalability, and operational maturity of data processing systems.
Build intelligent pipeline failure detection and recovery, including automated retry, root-cause assessment, escalation to on-call teams, and downstream user notification.
Develop AI-driven data quality and heuristic monitoring to detect anomalies, assess impact, and recommend or automate the next best action.
Design and evolve large-scale DataOps and platform capabilities that support enterprise data consumers and hundreds or more users.
Productionize AI within data engineering operations with appropriate evaluation, observability, human review, and fallback paths.
Partner with data engineering, platform, cloud, security, and business/data consumer teams to define requirements and drive technical solutions.
Lead architecture and design reviews and establish reusable engineering patterns and standards for resilient data operations.
Contribute hands-on to implementation, troubleshooting, and performance and scalability improvements across modern data platforms.
Evaluate and apply modern data and AI technologies based on fit, scalability, reliability, and operational value.
Mentor engineers and provide technical review and guidance on complex data platform and AI-enabled engineering problems.