AI Architect
Job Description
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
Key Responsibilities
1. Architecture & System Design
Core Engineering & Architecture
- 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
Key Responsibilities
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.
- Design and implement autonomous and semi-autonomous multi-agent systems using frameworks such as LangGraph, CrewAI, AutoGen, 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).
- 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.
- 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.
- 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 (DevEx) teams to embed AI into developer tooling and workflows.
- 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.
- 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 microservices.
- Demonstrated ability to design mission-critical, high-availability systems.
- Strong theoretical and hands-on expertise in:
- Deep Learning (CNN, RNN, LSTM)
- Transformer architectures and attention mechanisms
- Deep experience with Generative AI, including:
- Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and prompt engineering
- GANs and Diffusion models
- Proven experience integrating with OpenAI, Azure OpenAI, Hugging Face, or equivalent platforms.
- Expert-level proficiency in Python; strong working knowledge of C++ and Java.
- Extensive experience with PyTorch, TensorFlow, and Keras.
- Expertise in designing RESTful APIs, GraphQL, and event-driven architectures using Kafka or RabbitMQ.
- Strong understanding of databases, vector stores, and streaming systems.
- Proven track record of deploying and operating large-scale ML/AI workloads in production.
- Hands-on experience with Kubernetes, Docker, and Infrastructure as Code (IaC) tools (Terraform, Bicep, or CloudFormation).
- Familiarity with CI/CD pipelines, observability stacks, and secure cloud networking.
- Experience in Healthcare, Payer, or Life Sciences domains, including regulated data environments.
- Exposure to edge AI, on-device inference, or real-time decision-making systems.
- Contributions to open-source AI/ML projects or published technical thought leadership.
- Experience building internal AI platforms or AI Centers of Excellence (CoE).
- Enterprise-scale Generative AI platforms run reliably and efficiently in production.
- Autonomous agents delivering measurable productivity gains across the organization.
- Secure, governable, and cost-efficient AI ecosystems.
- Engineering teams are empowered by AI-native tooling and workflows.
- Clear architectural vision consistently aligns with strategic business outcomes.
Meet Your Recruiter
Perry Gross
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