Position: Lead / Principal Agentic AI Engineer
Employment Type: Full-Time
Experience: 8–10+ years overall, with 2+ years of LLM / Agentic AI experience
Location: Onsite – Phoenix, AZ / Johnston, RI / Dallas, TX
Level: Lead / Principal – Onsite Technical Lead
Role Overview
This is a senior technical leadership role where you will lead the architecture and delivery of enterprise Agentic AI platforms that automate document-intensive and decision-heavy workflows.
You will serve as the primary technical contact for the client, lead an onsite + offshore engineering team, drive architecture decisions, and remain hands-on with the most complex areas of the platform.
Key Responsibilities
- Design end-to-end Agentic AI and document-intelligence platforms.
- Architect RAG, retrieval, extraction, APIs, and multi-agent orchestration services.
- Design MCP connector frameworks and reusable source-integration patterns.
- Establish LLM governance, guardrails, evaluation frameworks, and human-in-the-loop workflows.
- Lead technical design reviews, coding standards, architecture, and delivery.
- Work directly with enterprise architects, security teams, and client stakeholders.
- Ensure scalability, resiliency, security, observability, and auditability of AI platforms.
- Lead and mentor onsite/offshore engineering teams.
Must-Have Skills
- 8–10+ years of software engineering experience.
- 2–3+ years of production LLM / GenAI / Agentic AI experience.
- Strong RAG expertise including chunking, embeddings, hybrid/semantic retrieval, reranking, and evaluation.
- Experience with MCP / Model Context Protocol, tool calling, or connector frameworks.
- Strong experience with LangGraph, Google ADK, or equivalent multi-agent frameworks.
- Strong Python and FastAPI development experience.
- Working knowledge of Java/Spring Boot and PostgreSQL.
- Experience with LLM evaluation, guardrails, prompt strategies, and hallucination mitigation.
- Proven technical leadership and client-facing architecture experience.
- Experience with security-constrained or regulated environments involving PII, RBAC, SSO, audit, and governance.
Nice to Have
- Self-hosted / on-prem LLM deployment and GPU-aware inference.
- Document AI / IDP / OCR and complex document extraction.
- OpenShift / Kubernetes.
- Qdrant / pgvector and vector database architecture.
- OpenTelemetry / Datadog.
- HashiCorp Vault / CyberArk.
- Fine-tuning / adapter training and ML model evaluation.
- Prior US onsite delivery and client leadership experience.
Technology Stack
AI/Agentic: Python, FastAPI, LangGraph, Google ADK, MCP
RAG: Embeddings, Qdrant, pgvector, OCR, document extraction
Backend: Java, Spring Boot, PostgreSQL, REST APIs
Platform: OpenShift, Kubernetes, OAuth2, Okta, Active Directory/RBAC
Observability: OpenTelemetry, Datadog
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