Gen AI Senior Engineer
Location: Charlotte, NC / Atlanta, GA / Minneapolis, MN / Irving, TX / Chicago, IL / Cincinnati, OH
Work Arrangement: Onsite – 5 Days a Week
Candidates local or nearby to any USB Hub location will be required to attend a face-to-face client interview.
Job Overview
We are seeking an experienced Senior Generative AI Engineer to design, develop, and deploy scalable Generative AI (GenAI) solutions within an enterprise environment, with a focus on AWS cloud enablement.
The ideal candidate will have strong hands-on expertise in Large Language Model (LLM)-based applications, GenAI architectures, Agentic AI workflows, and modern cloud-native engineering practices. This role will collaborate with cross-functional teams to build production-ready AI systems that meet enterprise standards for scalability, security, reliability, and performance.
Key Responsibilities
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Design, develop, and implement enterprise-grade Generative AI applications using LLMs, RAG architectures, prompt engineering, and Agentic AI concepts.
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Build intelligent AI pipelines and workflows using frameworks such as LangChain, LangGraph, and Microsoft Foundry Agent Service.
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Evaluate and optimize GenAI solutions for performance, accuracy, scalability, and reliability.
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Contribute to the end-to-end GenAI lifecycle, including solution design, development, integration, deployment, performance tuning, and optimization.
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Support secure deployment, horizontal scaling, and operational stability of GenAI workloads.
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Implement monitoring, logging, and observability practices for production AI environments.
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Develop and deploy GenAI systems across cloud platforms, particularly AWS and Azure.
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Contribute to distributed system design for scalable AI workloads.
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Implement containerization, orchestration, and Infrastructure as Code practices.
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Ensure enterprise solutions meet availability, performance, security, and reliability requirements.
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Develop scalable applications using Python and microservices-based architecture.
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Apply secure coding standards and appropriate data-handling practices for enterprise and regulated environments.
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Contribute to CI/CD pipelines, automated testing, and deployment workflows.
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Participate in code reviews and follow software engineering best practices.
Required Qualifications
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5+ years of experience developing and implementing GenAI applications leveraging:
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Large Language Models (LLMs)
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Retrieval-Augmented Generation (RAG)
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Prompt Engineering
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Agentic AI concepts and workflows
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5+ years of experience building intelligent pipelines using:
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5+ years of experience evaluating GenAI solution performance, accuracy, and scalability.
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5+ years of experience across the end-to-end GenAI lifecycle, including solution design, development, integration, deployment, and optimization.
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5+ years of experience supporting secure deployment, horizontal scaling, and operational stability of GenAI workloads.
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5+ years of experience with monitoring, logging, and observability practices.
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5+ years of experience developing and deploying GenAI systems across cloud platforms, including AWS and Azure.
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5+ years of experience with distributed system design for scalable AI workloads.
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5+ years of experience with modern infrastructure practices:
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Docker
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Kubernetes
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Terraform
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ARM/Bicep
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5+ years of experience developing scalable applications using Python and microservices architecture.
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5+ years of experience applying secure coding standards and data-handling practices in enterprise or regulated environments.
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5+ years of experience with CI/CD pipelines, automated testing, and deployment workflows.
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Experience participating in code reviews and following engineering best practices.
Education
Bachelor’s degree in Computer Science or equivalent work experience.
Work Location & Interview Requirements
Additional Information
Candidates should have strong hands-on GenAI engineering experience and the ability to build, deploy, and support secure, scalable, production-ready AI solutions in enterprise cloud environments.
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