Staff AI Engineer – Agentic AI and Automaton

Collective Health•San Francisco, TX

1d•Hybrid

About The Position

At Collective Health, we’re transforming how employers and their people engage with their health benefits by seamlessly integrating cutting-edge technology, compassionate service, and world-class user experience design. About the Role: We are looking for a seasoned Staff Engineer to lead the design and delivery of highly scalable, cloud-native backend, data, and AI systems with a strong focus on AI-driven automation and agentic workflows. In this role, you will define the technical direction for the core TPA platform (Eligibility, Provider Data and others), enable intelligent, end-to-end workflows across systems, and drive the integration of AI into real-world business operations. You will operate at the intersection of backend architecture, data engineering, and applied AI, while mentoring engineers and influencing technical strategy across teams. This is a hands-on leadership role for someone who thrives in ambiguous, high-impact problem spaces and can translate emerging AI capabilities into production-grade systems at scale.

Requirements

10+ years of experience building scalable, distributed backend systems and platforms
Strong expertise in Java/Spring Boot and/or Python, with deep understanding of microservices architecture
Proven experience designing and operating integration-heavy systems (APIs, event-driven systems, partner integrations)
Hands-on experience with cloud-native architectures and Implementation (AWS and/or GCP)
Strong experience building data pipelines and data-intensive systems (batch and/or streaming)
Deep understanding of data engineering principles (data modeling, quality, lineage, observability)
Experience working with complex data domains (e.g., transactional systems, EDI, or operational workflows)
Hands-on experience integrating AI/ML or LLM-based capabilities into production systems
Familiarity with RAG pipelines, embeddings, and vector-based retrieval systems
Solid understanding of event-driven architectures (Kafka, queues, SQS) and distributed systems design
Hands-on experience using AI-assisted development tools (e.g., Cursor, Claude Code, GitHub Copilot) to accelerate development
Strong understanding of AI-augmented engineering workflows, including prompt-driven development, testing acceleration, and iterative refinement
Proven ability to lead cross-team technical initiatives, influence architecture, and mentor senior engineers
Strong communication skills with the ability to connect technical decisions to business impact

Responsibilities

Set the technical vision and drive architecture for scalable, cloud-native backend, data, and AI systems
Design and build API-first and event-driven systems supporting internal and external integrations (partners, EDI, downstream platforms)
Lead development of high-throughput data pipelines (batch + streaming) powering operational workflows and AI use cases
Design and implement AI-driven automation and agentic workflows to reduce manual operations and enable intelligent decisioning
Integrate LLM-based capabilities (search, summarization, copilots, workflow orchestration) into core platform services
Establish best practices for AI systems (prompting, evaluation, guardrails, observability, responsible AI)
Build and evolve integration layers across APIs, events, and file-based systems (including complex partner integrations)
Improve data quality, validation, lineage, and real-time visibility across critical business workflows
Drive adoption of AI-assisted development workflows (e.g., Cursor, Claude Code, GitHub Copilot) to accelerate engineering velocity and delivery
Partner with Product, Data, and Operations to translate complex workflows into scalable, intelligent systems
Lead small but nimble teams, cross-team initiatives, influencing technical roadmap and AI adoption and implementation strategy
Mentor sr. engineers and technical leads, raising the bar on system design, data engineering, and AI adoption