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AI Architect

BravoTech

Nashville, TN

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JOB DETAILS
SKILLS
Amazon Web Services (AWS), Application Programming Interface (API), Architectural Services, Artificial Intelligence (AI), Automation, Best Practices, Biology, Business Processes, C++ Programming Language, Caching, Cloud Computing, Continuous Deployment/Delivery, Continuous Integration, Cost Control, Cost Modeling, Data Management, Data Science, Deep Learning, Design Patterns Programming Methodologies, Distributed Computing, Docker, Documentation, Ecosystems, GCP (Good Clinical Practices), GPU (Graphics Processing Unit), GraphQL, Healthcare, High Availability, Incident Management, Java, Kernel Programming, Knowledge Management, Leadership, Machine Tool, Memory Hardware, Memory Management, Mentoring, Microservices, Microsoft Windows Azure, Modeling Languages, Open Source, Performance Tuning/Optimization, Product Engineering, Programming Tools, Python Programming/Scripting Language, REST (Representational State Transfer), RabbitMQ, Refactoring, Scientific Research, Security Attacks, Service Level Agreement (SLA), Software Agents, Software Development Lifecycle (SDLC), Strategic Planning, System Architecture, Technical Publications, Test Automation, Thought Leadership, Validation Testing, Vendor/Supplier Evaluation, Vendor/Supplier Planning
LOCATION
Nashville, TN
POSTED
30+ days ago
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.

About the Company

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BravoTech

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