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.