Highlights
Multi-agent SDK / gateway FastAPI service hosting many agents behind path-prefix routing, per-agent tool registries, session-scoped conversational context. " Agent selection layer hybrid retrieval (vector RAG over a capability registry) plus closed-set LLM selection with JSON-schema-constrained outputs.
Numbers & Facts
| Location | Dallas, TX |
| Industry | Computer/IT Services |
| Company Size | 500 to 999 employees |
| Year Founded | 1990 |
| Website | http://q1tech.com/ |
Description
Location: Dallas, TX / Hybrid
Duration: 6 months
GBaMS ReqID: 10892199
Summary
Build and scale a production multi-agent AI platform serving thousands of internal users across multiple business units. Monthly release cadence, real users, real latency, real cost.
What You'll Own
" LLM-driven orchestrator that routes user intent across a portfolio of specialized agents delegation, memory, response validation, capability discovery.
" Agent selection layer hybrid retrieval (vector RAG over a capability registry) plus closed-set LLM selection with JSON-schema-constrained outputs.
" Multi-agent SDK / gateway FastAPI service hosting many agents behind path-prefix routing, per-agent tool registries, session-scoped conversational context.
" Tool-driven agents 15 30 tools per agent composed dynamically by an LLM; owns tool contracts, guardrails, and evaluation.
" Data API layer parameterized endpoints between agents and databases; LLMs never touch DBs directly.
" Partner-team onboarding versioned A2A contract, bring-your-own-agent registration, auto re-embedding.
Core AI Engineering
" Production LLM systems: RAG, tool/function-calling loops, structured outputs, hallucination guards, closed-set selection.
" Multi-agent orchestration: A2A protocols, session affinity, human-in-the-loop gating, kill switches, graceful degradation.
" Vector search + embeddings at scale (sub-second retrieval over thousands of docs).
" Evaluation & safety: PII/PHI masking, audit trails, feedback-loop instrumentation, offline + online eval.
Platform / Infrastructure
" Python 3.11+, FastAPI, async I/O, Pydantic.
" Modern LLM stacks (Gemini, GPT, Claude) and agent frameworks (LangGraph, Agent SDKs).
" Cloud (GCP or AWS): Kubernetes, object storage, workflow orchestration, Vertex/Bedrock-class services.
" Redis, MongoDB, Oracle/Postgres, SSO + RBAC.
" Observability: Prometheus, structured JSON logs, per-decision audit trails, p95 latency SLOs in seconds.
Skills: Digital : Python~Digital : Machine Learning~Digital : Artificial Intelligence(AI)~Generative AI
Duration: 6 months
GBaMS ReqID: 10892199
Summary
Build and scale a production multi-agent AI platform serving thousands of internal users across multiple business units. Monthly release cadence, real users, real latency, real cost.
What You'll Own
" LLM-driven orchestrator that routes user intent across a portfolio of specialized agents delegation, memory, response validation, capability discovery.
" Agent selection layer hybrid retrieval (vector RAG over a capability registry) plus closed-set LLM selection with JSON-schema-constrained outputs.
" Multi-agent SDK / gateway FastAPI service hosting many agents behind path-prefix routing, per-agent tool registries, session-scoped conversational context.
" Tool-driven agents 15 30 tools per agent composed dynamically by an LLM; owns tool contracts, guardrails, and evaluation.
" Data API layer parameterized endpoints between agents and databases; LLMs never touch DBs directly.
" Partner-team onboarding versioned A2A contract, bring-your-own-agent registration, auto re-embedding.
Core AI Engineering
" Production LLM systems: RAG, tool/function-calling loops, structured outputs, hallucination guards, closed-set selection.
" Multi-agent orchestration: A2A protocols, session affinity, human-in-the-loop gating, kill switches, graceful degradation.
" Vector search + embeddings at scale (sub-second retrieval over thousands of docs).
" Evaluation & safety: PII/PHI masking, audit trails, feedback-loop instrumentation, offline + online eval.
Platform / Infrastructure
" Python 3.11+, FastAPI, async I/O, Pydantic.
" Modern LLM stacks (Gemini, GPT, Claude) and agent frameworks (LangGraph, Agent SDKs).
" Cloud (GCP or AWS): Kubernetes, object storage, workflow orchestration, Vertex/Bedrock-class services.
" Redis, MongoDB, Oracle/Postgres, SSO + RBAC.
" Observability: Prometheus, structured JSON logs, per-decision audit trails, p95 latency SLOs in seconds.
Skills: Digital : Python~Digital : Machine Learning~Digital : Artificial Intelligence(AI)~Generative AI
About Company
Q1 consists of experienced and recognized experts providing the capability to respond to market demand in order to provide professional services for our clients including Enterprise software implementations, application integration and technical / functional support.
Q1 has steadily grown into a Quality IT services and solutions organization with the average experience of our team being over 10 years. We have continuously met or exceeded client expectations by delivering professional services and project implementations on time and under budget to help clients truly recognize return on investment.
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