Retrieval Augmented Generation Jobs in Arizona (NOW HIRING)

Role - Senior Full-Stack Engineer (Node.js / Angular / MongoDB / Camunda) - Agentic Engineering
Location - Phoenix, AZ (Hybrid)
Duration - Long Term contract
Role summary
We're looking for a seasoned full-stack engineer who will own features end-to-end and operate with an "agentic engineering" approach: independently designing, building, and delivering production systems while leveraging AI and autonomous agent patterns to accelerate development, testing, and operations. You will combine traditional engineering (Node.js backend, Angular front end, MongoDB, Camunda workflows) with LLMs, tool integration, and model ops practices to create robust, observable, and secure solutions.
What you'll do (responsibilities)
- Independently drive end-to-end delivery of features and products: requirements → architecture → implementation → testing → deployment → monitoring → iteration.
- Use AI/LLM capabilities and agentic patterns to accelerate design, coding, test generation, documentation, and troubleshooting while ensuring human oversight, correctness, and security.
- Design and implement scalable RESTful APIs and microservices in Node.js (Express, NestJS, or similar) with production-quality CI/CD, observability, and automated testing.
- Build responsive, accessible Angular applications with component architecture, state management, routing, and automated tests.
- Model, implement, and optimize MongoDB schemas, aggregation pipelines, and indexing strategies; design for performance, backup/restore, and scaling.
- Design and implement business processes and long-running workflows in Camunda (BPMN), integrating timers, DMN decision tables, and external workers.
- Integrate LLMs and agent frameworks where appropriate: RAG (retrieval-augmented generation), tool invocation, structured outputs (JSON), and safe fallbacks for critical paths.
- Implement model orchestration and model ops practices: prompt/version management, testing/validation, A/B or shadow testing, telemetry, drift detection, and rollbacks.
- Build automation that uses autonomous agents for routine tasks (test scaffolding, codegen, data exploration) while ensuring review gates and traceability.
- Ensure security, privacy, and compliance of AI-powered features: input/output filtering, PII management, access control, and model usage auditing.
- Own CI/CD pipelines and deployment automation; containerization (Docker) and Kubernetes orchestration for services and workers.
- Mentor engineers and evangelize safe, effective use of AI-assisted development across the team.
- Maintain clear technical documentation: API specs (OpenAPI/Swagger), BPMN diagrams, data models, model/agent runbooks, and acceptance criteria.
Required qualifications
- 6+ years of professional software engineering experience with proven full-stack delivery.
- Strong Node.js experience and modern backend design (async patterns, testing, API design).
- Strong Angular experience for building production UIs (components, RxJS, forms, testing).
- Hands-on MongoDB experience: schema design, aggregation framework, indexing, replication/sharding fundamentals.
- Practical Camunda (or equivalent BPMN engine) experience: modeling flows, external workers, timers, and DMN.
- Demonstrated experience using LLMs and AI tooling in production (prompt engineering, RAG, tool integration, or agent frameworks).
- Familiarity with model ops: versioning, testing, monitoring, telemetry for model/service behavior.
- Experience designing agentic or automation workflows while ensuring human-in-the-loop controls and auditability.
- Experience with CI/CD, containerization (Docker), and orchestration (Kubernetes desirable).
- Test-first mindset and experience with unit, integration, and E2E testing frameworks.
- Strong understanding of security best practices (auth/authorization, secrets management, OWASP) and data privacy for AI workloads.
- Excellent written and verbal communication; able to write technical specs and explain AI/agent tradeoffs to stakeholders.
- Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.
Behavioral / Team fit
- Autonomous and outcome-driven: comfortable working solo to deliver complex features with minimal oversight.
- Pragmatic about risk: balances AI augmentation with rigorous validation, safety, and human oversight.
- Collaborative: communicates clearly with product, design, security, and operations.
- Mentorship mindset: shares best practices and supports teammates in adopting agentic tools responsibly.