Nashville
Role Overview: We are 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 • 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., • 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)., • 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., • 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., • 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., • 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., • 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. Core Engineering & Architecture • 12+ years of experience in enterprise-grade full-stack or platform architecture., • Strong background in product engineering, distributed systems, and microservices., • 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, • 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., • 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)., • 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., • 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.