Generative AI Engineer

Job Title:

Generative AI Engineer Location:

Charlotte, NC Pay:

[Full-Time / Contract]

Job Overview:

We are seeking a

Generative AI Engineer

with deep technical expertise across Generative AI, MLOps, and scalable distributed systems. In this role, you'll lead the design, development, deployment, and optimization of AI/ML solutions powered by LLMs, embedding models, and Retrieval-Augmented Generation (RAG) frameworks. You'll also be instrumental in driving production-grade MLOps workflows, managing big data pipelines, and integrating cloud-native tools across AWS, GCP, and Azure.

You'll work closely with cross-functional teams and mentor engineers to deliver transformative AI solutions for enterprise environments, including the Microsoft ecosystem.

Key Responsibilities: Design and implement

Generative AI solutions

using LLMs, vector databases, embedding models, vector search, and RAG techniques. Build and maintain robust

MLOps pipelines

for model training, testing, and deployment using

AWS SageMaker ,

Ray , and modern CI/CD practices. Engineer

distributed data pipelines

and streaming systems using

Apache Spark ,

Kafka ,

Hadoop ,

HBase , and

Cassandra . Apply

machine learning

and

deep learning

frameworks such as

Scikit-learn ,

TensorFlow ,

Keras , and

Spark MLlib . Conduct advanced

NLP

tasks using

spaCy ,

nltk , and embedding strategies. Analyze large datasets using the

Python data ecosystem

(Pandas, Scikit-learn, etc.). Optimize performance of distributed systems and applications in

Python ,

Java , and

Scala . Manage and scale

data stores : vector stores (e.g.,

Milvus ,

MongoDB Atlas ), NoSQL (e.g.,

Redis ,

Cassandra ), and SQL (e.g.,

Postgres ,

MySQL ). Utilize DevOps and infrastructure tools:

Docker ,

Kubernetes ,

Ansible ,

Terraform ,

Linux , and

Git . Monitor and tune the performance of AI models, applications, and distributed systems. Lead, mentor, and inspire high-performing technical teams. Collaborate on AI integrations with

Microsoft Copilot Studio ,

Power Platform , and

Dynamics 365 . Required Qualifications:

7+ years of experience in AI/ML, data engineering, or distributed systems. Proven experience with

LLMs ,

RAG architectures , and

vector databases . Hands-on expertise with

MLOps

tools, especially

SageMaker

and

Ray . Strong programming skills in

Python ,

Java ,

Scala , and

R . Solid understanding of

cloud platforms : AWS (preferred), GCP, and Azure. Demonstrated success leading engineering teams or AI initiatives. Proficient with modern

DevOps

toolchains and best practices. Preferred Qualifications:

Experience with enterprise AI integrations using

Microsoft Copilot Studio ,

Power Platform , or

Dynamics 365 . Background in building real-time AI solutions and observability tooling. Contributions to open-source AI projects or research in Generative AI.