Full stack with Python AI/ML || Dallas TX

AI/ML with Python full stack

Dallas TX-Hybrid

AI/ML with Python full stack with 8 to 9 years of relevant experience. Who are really hands on experience and can work independently.

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 (Google Cloud Platform 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-Fast

• Comfortable 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 Discipline

• Uses 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 If

• You'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.

Bonus

• Contributions 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 Expectations

• Strong 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.