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Job Title:
AI Architect
Location:
Nashville, TN
Duration: Long Term
Job Summary:
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Manage multiple concurrent initiatives while balancing innovation with reliable delivery.
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Drive buy-vs-build decisions, vendor evaluations, and strategic roadmap planning.
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Evangelize AI best practices across engineering, product, and data teams.
Required Qualifications
Core Engineering & Architecture
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12+ years of experience in enterprise-grade full-stack or platform architecture.
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Strong background in product engineering, distributed systems, and microservices.
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Demonstrated ability to design mission-critical, high-availability systems.
AI / ML & Generative AI Expertise
Strong theoretical and hands-on expertise in:
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Deep Learning (CNN, RNN, LSTM)
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Transformer architectures and attention mechanisms
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Deep experience with Generative AI, including:
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Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and prompt engineering
GANs and Diffusion models
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Proven experience integrating with OpenAI, Azure OpenAI, Hugging Face, or equivalent platforms.
Technical Stack
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Expert-level proficiency in Python; strong working knowledge of C++ and Java.
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Extensive experience with PyTorch, TensorFlow, and Keras.
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Expertise in designing RESTful APIs, GraphQL, and event-driven architectures using Kafka or RabbitMQ.
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Strong understanding of databases, vector stores, and streaming systems.
Cloud & DevOps
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Proven track record of deploying and operating large-scale ML/AI workloads in production.
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Hands-on experience with Kubernetes, Docker, and Infrastructure as Code (IaC) tools (Terraform, Bicep, or CloudFormation).
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Familiarity with CI/CD pipelines, observability stacks, and secure cloud networking.
Preferred Other Skills
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Experience in Healthcare, Payer, or Life Sciences domains, including regulated data environments.
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Exposure to edge AI, on-device inference, or real-time decision-making systems.
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Contributions to open-source AI/ML projects or published technical thought leadership.
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Experience building internal AI platforms or AI Centers of Excellence (CoE).
What Success Looks Like
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Enterprise-scale Generative AI platforms run reliably and efficiently in production.
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Autonomous agents delivering measurable productivity gains across the organization.
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Secure, governable, and cost-efficient AI ecosystems.
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Engineering teams are empowered by AI-native tooling and workflows.
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Clear architectural vision consistently aligns with strategic business outcomes.
Regards,
Deepika Dua
VBeyond Corporation
https://www.linkedin.com/in/deepika-dua-018459166/
E: Deep...@vbeyond.com | www.vbeyond.com
390 Amwell Road, Suite # 107, Hillsborough, NJ 08844

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