AI Architect - Local to Dallas, TX

The ideal candidate is a hands‑on AI Architect with deep expertise in LLM‑based systems, agentic architectures, and AWS‑native platforms, combined with strong experience in guardrails, evaluation frameworks, and cost optimization. This role requires a balance of advanced technical depth and architectural leadership to deliver scalable, production‑grade AI platforms.

Architect and deliver enterprise‑scale AI systems leveraging Large Language Models (LLMs
), including Retrieval‑Augmented Generation (RAG), fine‑tuning strategies, and advanced agentic architectures.

Design multi‑agent systems capable of tool orchestration, planning, reasoning, and context management for complex, distributed workflows.

Design and implement guardrails for LLM applications, including content filtering, prompt injection mitigation, hallucination reduction, and policy enforcement.

Experience with safety frameworks, human‑in‑the‑loop validation, and governance controls to ensure compliance, reliability, and ethical AI usage.

Optimize LLM workloads for cost, latency, and throughput, including model selection strategies, prompt engineering efficiency, caching mechanisms, batching, and token optimization.

Experience implementing cost monitoring, usage controls, and scalable inference architectures to balance performance with operational efficiency.

Develop and operationalize evaluation (evals) frameworks for LLMs, including automated benchmarking, prompt evaluation, response scoring, and regression testing.

Experience with both offline and online evaluation techniques, including human evaluation pipelines, A/B testing, and continuous feedback loops to improve model quality and reliability.

Hands‑on experience with Amazon Bedrock, including foundation model evaluation, prompt engineering, orchestration, and model lifecycle management.

Experience with Agent Core or similar agent frameworks for building composable, stateful, and tool‑augmented AI agents with memory and execution control.

Expert‑level proficiency in Python with deep experience across AI/ML ecosystems (PyTorch, Tensor Flow, Hugging Face, Lang Chain, or equivalent).

Strong experience building scalable, production‑grade APIs using FastAPI, including async processing, model inference optimization, and microservices architecture.

Design and implement robust CI/CD pipelines for AI applications, including automated model testing, validation, reproducibility, and staged rollouts.

Experience with MLOps frameworks, model versioning, feature stores, and continuous monitoring using tools such as Git Hub Actions, Jenkins, AWS Code Pipeline, and Terraform/Cloud Formation.

Basic expertise in Amazon VPC design, including subnetting strategies, private networking, security segmentation, and high‑availability architecture for AI workloads.

Hands‑on experience with AWS Lambda and event‑driven patterns for scalable, serverless inference pipelines and integration with AWS services (API Gateway, SQS, Step Functions).

Proven experience leading distributed engineering and AI development teams across multiple time zones, ensuring high productivity and alignment.

Strong track record of implementing agile delivery models, managing cross‑regional collaboration, and driving continuous delivery across globally dispersed teams.