Full stack with Python AI/ML

One of our ;leading client is looking for Full stack with Python AI/ML in Dallas TXSummaryBuild 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 OwnLLM-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 EngineeringProduction 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 / InfrastructurePython 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.Ways of Working — Fast Turnaround, Ship-FastComfortable with short cycle times: spec → design → merged → deployed in days, not sprints. Monthly releases are the floor, not the ceiling.Bias to ship the smallest correct thing, verify in production, iterate. No polish before proof.Owns the full loop: intake → spec → design → implementation → code review → test evidence → UAT → deploy → post-release observation.Fluent with AI-assisted developer tooling (Claude Code, Cursor, agentic IDEs); reads and writes code with an LLM in the loop as a force multiplier.Skill Curation & Reuse — Agentic Development DisciplineUses and extends the team's agentic SDLC skill library — capability intake, spec authoring, design docs, implementation plans, release-impact artifacts, deployment records.Curates new skills when a workflow repeats: codifies patterns (accessibility, security/STRIDE, CI/CD, data-source adapters, renderer standards) into reusable skills the whole team can invoke.Treats skills, prompts, and evals as first-class artifacts — versioned, reviewed, and improved like code.Knows when to reach for a skill vs. write ad-hoc: standard flows for standard work, creative bandwidth saved for novel problems.You'll Thrive Here IfYou've shipped LLM agents in production (not demos) with real users, latency, and cost constraints.You reason about routing, tool selection, and context strategy as first-class design surfaces — not just prompt tuning.You own both the model layer and the platform underneath it (queues, auth, secrets, deploy, K8s).You move fast without breaking discipline: verification before completion, evidence before assertions.Regulated-domain experience (healthcare, financial services) is a plus.BonusContributions to agent frameworks, evaluation harnesses, or open A2A protocols.Patent / IP work in agentic systems, RAG, or multi-agent orchestration.Prior experience authoring internal skill libraries, agent playbooks, or SDLC automation for AI teams.Role ExpectationsStrong hands-on experience in designing, developing, and implementing Agentic AI solutions and frameworks at enterprise scale.Proven track record of delivering Agentic AI use cases in production environments, demonstrating measurable business value and outcomes.Ability to work independently with a high degree of ownership, accountability, and self-motivation.Experience leading or contributing to large-scale digital transformation initiatives for Fortune 500 and enterprise clients.Strong communication skills with the ability to clearly articulate solution approaches, business use cases, technical contributions, and delivered impact.Deep understanding of AI solution architecture, with the capability to confidently explain design decisions, technology choices, challenges, mitigations, and results.Demonstrated capability to conceptualize, architect, and build AI-driven solutions end-to-end, from ideation through deployment and adoption.Ability to collaborate effectively with business and technology stakeholders while driving innovation and delivering tangible business outcomes.