AI Engineer – Agentic AI and Automaton
Listed on 2026-06-18
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Software Development
AI Engineer (Applied/Software), Backend Developer, Software Architect, Cloud Engineer - Software
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.
What you’ll do:- 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, Git Hub 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
- 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, Git Hub 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
- Backend:
Java / Spring Boot, Python - Cloud: AWS, GCP
- Orchestration:
Kubernetes, Docker - Databases:
PostgreSQL - Frontend:
React, Angular - AI Tooling:
Claude Code, Git Hub Copilot, Cursor, Gemini, and emerging AI…
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