Lead / Principal Agentic AI Engineer

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