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

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.

2. Agentic Systems & Workflow Orchestration

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).

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 (DevEx) 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.

Required Qualifications

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.

AI / ML & Generative AI Expertise

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.

Technical Stack

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.

Cloud & DevOps

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.

Preferred Other Skills

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).

What Success Looks Like

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.