AI Technical Architect-Dallas,TX-Onsite

[David Miller]

Job Title: AI Technical Architect
Location: Dallas, TX, - Hybrid
Contract

Key Responsibilities

• Define AI/ML reference architecture and solution blueprints (batch/streaming ML, LLM + RAG, multimodal).
• Lead end-to-end solution design across data ingestion, model training, inference, deployment, and monitoring.
• Architect LLM applications (agents, summarization, classification) with RAG, evaluation frameworks, safety controls, and guardrails.
• Own MLOps/LLMOps practices, including CI/CD for models, model registry, feature stores, lineage tracking, observability, drift detection, and cost monitoring.
• Choose the right cloud and runtime strategy (managed services vs. self-hosted, GPU vs. CPU, serverless vs. containerized).
• Establish AI governance standards, including PII handling, encryption, auditability, and Responsible AI practices.
• Collaborate with product and business stakeholders to translate requirements into architectural decisions and delivery plans.
• Perform technical spikes and POCs, benchmark models and infrastructure, and lead Architecture Reviews.
• Create and maintain standards, patterns, and reusable components; mentor engineers across teams.
• Drive performance and cost optimization initiatives, including throughput, latency, SLA/SLO management, caching, quantization/distillation, and autoscaling.
• Support vendor and product evaluations, including cloud AI services, vector databases, orchestration frameworks, and monitoring platforms.

Required Qualifications

• Bachelor’s or master’s degree in computer science, Engineering, Data Science, AI, or a related field.
• 15+ years of overall engineering experience, with at least 4+ years in AI/ML solution architecture.
• Proven experience designing and deploying AI systems in production at scale (LLM and/or classical ML).
• Strong hands-on proficiency in Python and at least one major cloud platform (AWS, Azure, or GCP).

Must-Have Technical Skills

AI/ML & LLM Architecture

• Designing LLM/RAG systems, including retrieval pipelines, chunking strategies, embeddings, reranking, prompt orchestration, response orchestration, evaluation, and safety.
• Deep understanding of the model lifecycle, including fine-tuning, PEFT/LoRA, quantization, distillation, latency optimization, and cost optimization.
• Strong ML/NLP expertise, including feature engineering, model selection, training, cross-validation, experimentation, and testing.

MLOps / LLMOps

• CI/CD for ML, including model versioning, model promotion, feature stores, model registry, lineage tracking, and drift detection.
• Inference stacks including PyTorch, TensorFlow, vLLM, TGI, ONNX, GPU orchestration, autoscaling, and APM.
• Pipelines and orchestration frameworks such as Airflow, Kubeflow, and MLflow.

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