AI Architect

AI Architect – Hybrid Onsite (2 days/ week) in Nashville TN

Seeking a visionary AI Architect to lead the design, governance, and implementation of next-generation Generative AI and Agentic Systems across the enterprise. This role is responsible for translating complex business problems into scalable, secure, and production‑grade AI solutions, with a strong emphasis on autonomous agents, intelligent workflows, and AI‑augmented SDLC ecosystems.

The ideal candidate brings a rare combination of enterprise‑scale system architecture expertise, deep Generative AI knowledge, and hands‑on engineering leadership, enabling them to operate seamlessly across strategy, design, and execution phases.

Years of

Experience:

12+ Years

• 1. Architecture & System Design
  • Own the end‑to‑end architecture of large‑scale, distributed GenAI platforms, including microservices, data pipelines, and AI inference layers.
  • Define reference architectures and design patterns for Generative AI, agentic workflows, and AI‑enabled enterprise platforms.
  • Ensure all systems are secure, scalable, fault‑tolerant, cost‑efficient, and production‑ready.
• 2. Agentic Systems & Workflow Orchestration
  • Design and implement autonomous and semi‑autonomous multi‑agent systems using frameworks such as Lang Graph, CrewAI, Auto Gen, Semantic Kernel, or custom orchestration engines.
  • Enable agent collaboration, task planning, memory management, tool use, and self‑reflection capabilities.
  • Architect agent‑driven enterprise workflows (e.g., code generation, testing, incident triage, knowledge discovery, and business process automation).
• 3. Generative Model Engineering
  • Lead model selection, fine‑tuning, and optimization of Large Language Models (LLMs) and Small Language Models (SLMs), including OpenAI, Anthropic, Gemini, LLaMA, Mistral, and domain‑specific models.
  • Apply Parameter‑Efficient Fine‑Tuning (PEFT) techniques such as LoRA, QLoRA, adapters, and distillation to optimize cost and performance.
  • Oversee Retrieval‑Augmented Generation (RAG) architectures, vector search, prompt engineering, memory augmentation, and evaluation pipelines.
  • Drive experimentation with Diffusion models, GANs, and multimodal models where applicable.
• 4. LLMOps / MLOps & Cloud Infrastructure
  • Architect and standardize LLMOps/MLOps pipelines for training, evaluation, deployment, observability, and lifecycle management.
  • Design cloud‑native AI platforms on AWS, Azure, or GCP, leveraging GPU/TPU infrastructure, Kubernetes, and serverless computing patterns.
  • Implement comprehensive monitoring for latency, hallucinations, model drift, cost usage, security events, and SLA compliance.
  • Optimize inference using techniques such as quantization, batching, caching, and intelligent model routing.
• 5. AI‑Driven SDLC & Developer Experience
  • Architect AI‑augmented Software Development Lifecycle (SDLC) systems, including:
    • Agentic code generation and refactoring
    • Automated test generation and validation
    • Intelligent CI/CD workflows
    • AI‑powered documentation and knowledge management
  • Partner with platform and Developer Experience (Dev Ex) teams to embed AI into developer tooling and workflows.
• 6. Governance, Security & Responsible AI
  • Define AI governance frameworks covering model risk, data privacy, lineage, explainability, bias detection, and regulatory compliance.
  • Ensure alignment with security, legal, and regulatory requirements (e.g., HIPAA, SOC2, GDPR, as applicable).
  • Establish robust guardrails for safe agent behavior, access control, prompt injection defense, and data leakage prevention.
• 7. Strategy, Leadership & Collaboration
  • Serve as a technical thought leader and advisor to executive stakeholders.
  • Lead and mentor senior engineers, data scientists, and AI researchers.
  • Manage multiple concurrent initiatives while balancing innovation with reliable delivery.
  • Drive buy‑vs‑build decisions, vendor evaluations, and strategic roadmap planning.
  • Evangelize AI best practices across engineering, product, and data teams.

• Core Engineering & Architecture
  • 12+ years of experience in enterprise‑grade full‑stack or platform architecture.
  • Strong background in product engineering, distributed systems, and…