Principal Engineer, Transformation Engineering

Mosaic Health

2d•$131,243 - $196,865

About The Position

Mosaic Health is seeking a hands-on individual contributor with applied AI experience and strong knowledge of technology infrastructure and data pipelines. The engineer will build applications, integrations, and automated workflows across apree, CareMore, and Millennium Physician Group to address operational needs, improve service, reduce manual effort, and support the business. This is not a people-management role. The role takes solutions from discovery through rapid prototyping, pilot deployment, and value validation, then partners with IT, data, security, and Technology teams to industrialize approved solutions. It involves substantial hands-on work designing, writing, testing, and troubleshooting code, including supporting data pipelines. The engineer will focus on prioritized initiatives, account for workflow and system differences across the businesses, and create reusable capabilities that can be adapted to local needs.

Requirements

Typically 5–7 years of software engineering experience, including substantial hands-on experience building and deploying AI-enabled applications.
Demonstrated ownership of production systems and ability to explain technical decisions, failures, and improvements.
Experience taking a product or workflow from initial idea through pilot deployment and value validation, integrating language models into real applications, and partnering on industrialization and ongoing improvement.
Strong backend development skills in Python and/or TypeScript; proficiency in SQL; experience designing APIs, services, and database schemas.
Practical understanding of language model APIs, embeddings, retrieval-augmented generation, structured outputs, and tool calling, including designing workflows around model limitations.
Experience evaluating AI systems with representative datasets and measurable criteria, including retrieval accuracy, unsupported outputs, edge cases, and regression testing; ability to balance quality, latency, and cost.
Deep understanding of cloud infrastructure and distributed applications, including compute, storage, networking, identity and access management, containers, queues, observability, and CI/CD; experience diagnosing production issues and making reliability, security, and cost tradeoffs.
Ability to independently break ambiguous problems into steps, make sound technical decisions, learn unfamiliar tools, implement solutions, and provide support; recognizes when to seek input or escalate dependencies.
Collaborative working style, including listening to users, incorporating feedback, sharing knowledge, communicating progress and tradeoffs clearly, and following through on commitments.
Hands-on experience designing and operating data pipelines, including ETL/ELT, batch and event-driven processing, orchestration, data validation, and monitoring; understanding of source-system behavior, data freshness, lineage, and schema changes.
Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related field required.
5–7 years of progressive software engineering experience, including significant experience serving as a technical lead, software architect, or principal engineer responsible for enterprise-scale software platforms.
Expert-level knowledge of software architecture, distributed systems, cloud-native application development, microservices, API design, engineering automation, CI/CD, secure software development, and enterprise application design.
Demonstrated expertise establishing engineering standards for AI-assisted software development, including responsible AI adoption, code provenance, data privacy, software quality, and engineering governance.
Deep experience implementing and advancing full-stack observability, including application performance monitoring, distributed tracing, telemetry, logging, cloud monitoring, and operational analytics across complex production environments.
Proven ability to influence enterprise technology strategy, lead cross-functional engineering initiatives, establish technical standards, and build alignment across Product Management, Enterprise Architecture, Infrastructure, Platform Engineering, Information Security, and executive technology leadership.
Exceptional analytical, communication, mentoring, and technical leadership skills; technical adaptability and experience evaluating, introducing, and operationalizing emerging software engineering technologies and industry best practices.

Nice To Haves

Advanced degree preferred.

Responsibilities

Work with operational teams to understand workflows, handoffs, exceptions, and data sources; translate agreed business objectives into practical technical plans and drive next steps when requirements are incomplete.
Build, collaborate on, and maintain AI-enabled applications, backend services, and integrations, using language models, retrieval-augmented generation, structured outputs, and tool calling when appropriate to defined operational problems.
Design and maintain pipelines that ingest, transform, validate, and deliver structured and unstructured data; manage data quality, schema changes, retries, duplicate records, and backfills, and make lineage and failures traceable.
Prototype and evaluate models, frameworks, and software tools; compare AI approaches with conventional software and existing products and recommend solutions based on measured quality, latency, cost, maintainability, and operational fit.
Build evaluation datasets and automated tests for model outputs, retrieval quality, and end-to-end workflows.
Partner with IT, data, and security teams on approved architecture, access, integrations, and deployment; build pilots in approved environments and help transition approved solutions to scalable operation with appropriate security and operational controls.
Collaborate with consultants and vendors while retaining hands-on ownership; review designs and code, contribute to implementation, and ensure documentation and knowledge transfer support maintenance and extension.
Work with business owners and Finance partners on pilot rollout, user feedback, and value validation; establish baselines and track adoption, cycle time, manual effort, error rates, and operational and financial value to assess whether solutions should scale.
Take initiative and deliver with limited day-to-day direction, seeking input or escalating dependencies when appropriate and communicating progress, decisions, and tradeoffs.
Collaborate across disciplines, listen to frontline users, incorporate feedback, share knowledge, and follow through on commitments.