Software Engineer

Location: Charlotte, NC
Salary: $69.00 USD Hourly - $74.00 USD Hourly
Description:
Job Title: Software Engineer - AI/ML & LLM (Contingent Assignment)
Location: Charlotte, NC
Job Type: Contingent/Contract
About the Role

We are seeking a highly skilled and experienced Software Engineer with a strong background in AI/ML and Large Language Models (LLMs) to support complex, large-scale software engineering initiatives. In this role, you will consult on strategic projects, analyze multifaceted technical challenges, and contribute to the development and deployment of cutting-edge AI solutions. You will collaborate closely with cross-functional teams and client stakeholders to deliver high-impact outcomes.
Key Responsibilities

• Lead and consult on complex software engineering initiatives with broad organizational impact.
• Analyze and resolve large-scale, multi-dimensional engineering challenges involving novel or unprecedented factors.
• Design and implement scalable, cloud-native APIs and services for AI/ML applications.
• Optimize and deploy AI models on GPU clusters, leveraging distributed computing frameworks.
• Collaborate with client teams to ensure alignment with business goals, compliance standards, and technical requirements.

Minimum Qualifications

• 5+ years of experience in Software Engineering or equivalent (including consulting, military, or academic experience).
• 3+ years of experience with cloud computing platforms (e.g., Google Cloud Platform, AWS, Azure).
• 3+ years of experience working with GPU clusters for AI/ML workloads.
• 2+ years of hands-on experience with Large Language Models (LLMs).
• 2+ years of experience with Apache Iceberg or similar data lake technologies.

Preferred Qualifications

• Proficiency in Python, FastAPI, Django, and cloud-native development.
• Experience with Generative AI frameworks such as LLaMA, Mistral, etc.
• Familiarity with FastAPI, Unicorn, Swagger for API development.
• Expertise in Apache Spark (PySpark), Apache Kafka, and Kubernetes.
• Experience with multi-GPU training and distributed computing frameworks (e.g., TensorFlow Distributed, PyTorch Distributed, Horovod).
• Strong understanding of NVIDIA GPU configuration and Google Cloud Platform infrastructure (TPUs, GPU instances).

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