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Lead / Principal Agentic AI Engineer - W2

Cyma Systems IncDallas, TX🇺🇸United StatesPosted Oct 13, 2026

Quick Overview

Seniority

Leader

Work mode

On Site

Location

Dallas, TX, United States

Posted

19 hours ago

FastAPISpringSpring BootSSODatadogJavaKubernetesLLMPostgreSQLPythonVault

Job Description

Lead / Principal Agentic AI Engineer (Onsite Technical Lead)

US Onsite (preferred - Pheonix, AZ, Johnson, RI, Dallas)

need on w2

8 10 years software engineering, with 3+ years architecting and shipping LLM / GenAI / agentic systems in production.
Deep RAG experience: you've designed retrieval systems at scale chunking and indexing strategy, embedding-model selection, hybrid/semantic retrieval, re-ranking, evaluation and know the failure modes cold.
MCP & connector experience: proven design of tool-calling / connector frameworks; able to define a reusable, config-driven onboarding pattern for heterogeneous internal and external sources.
Multi-agent orchestration: expert with LangGraph / Google ADK / equivalent; you can reason about agent topology, state, retries, determinism, and where NOT to use an agent.
Strong Python + FastAPI for production services; solid grasp of Java/Spring Boot and PostgreSQL to lead integration across a polyglot stack.
LLM evaluation, guardrails, prompt strategy, and hallucination control as first-class engineering disciplines not afterthoughts.
Demonstrated technical leadership: led engineering pods, set standards, mentored seniors, and leading the client/stakeholder relationship.
Experience delivering in regulated / security-constrained environments: audit, PII, SSO/RBAC, model governance.
Excellent executive-level communication you'll be the technical voice in the room with the client.

Preferred skills

ML modeling depth: fine-tuning / adapter training, embedding-model evaluation and selection, classical ML for classification and confidence scoring, and building rigorous evaluation harnesses.
Self-hosted / on-prem LLM serving experience (inference optimization, GPU-aware deployment, PII-safe inference).
Document-AI / IDP depth: layout-aware extraction, OCR pipelines, footnote/table extraction from complex forms.
OpenShift / Kubernetes architecture, OpenTelemetry, Datadog, and enterprise secrets management (HashiCorp Vault / CyberArk).
Vector database architecture and tuning at scale (Qdrant, pgvector).
Prior onsite delivery / client-lead experience in the US

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