Machine Learning Engineer (Generative AI)
Strategic Staffing Solutions
Machine Learning Engineer (Generative AI)
Location: Charlotte, NC (Hybrid)
Duration: 12 Month Contract
Employment Type: W2 Only
Overview
We are seeking a highly skilled Machine Learning Engineer specializing in Generative AI to design, develop, and deploy cutting-edge AI solutions that drive innovation across the enterprise. This role will focus on building scalable AI applications utilizing Large Language Models (LLMs), retrieval-augmented generation (RAG), agentic AI frameworks, and modern machine learning technologies.
The ideal candidate combines strong software engineering fundamentals with hands-on experience developing and deploying production-grade AI solutions. This individual will partner closely with engineering teams, architects, and business stakeholders to build intelligent systems that solve complex business challenges and support enterprise-scale initiatives.
Key Responsibilities
Location: Charlotte, NC (Hybrid)
Duration: 12 Month Contract
Employment Type: W2 Only
Overview
We are seeking a highly skilled Machine Learning Engineer specializing in Generative AI to design, develop, and deploy cutting-edge AI solutions that drive innovation across the enterprise. This role will focus on building scalable AI applications utilizing Large Language Models (LLMs), retrieval-augmented generation (RAG), agentic AI frameworks, and modern machine learning technologies.
The ideal candidate combines strong software engineering fundamentals with hands-on experience developing and deploying production-grade AI solutions. This individual will partner closely with engineering teams, architects, and business stakeholders to build intelligent systems that solve complex business challenges and support enterprise-scale initiatives.
Key Responsibilities
- Design, develop, test, and deploy Generative AI solutions for text, image, and multimodal applications.
- Build and optimize Large Language Model (LLM) applications using modern AI frameworks and tooling.
- Develop advanced prompt engineering strategies and context-aware AI workflows.
- Design and implement Retrieval-Augmented Generation (RAG) architectures utilizing vector databases and semantic search techniques.
- Build agentic AI applications leveraging multi-agent frameworks, memory management, session handling, and Model Context Protocol (MCP) tools.
- Integrate AI capabilities into enterprise applications, APIs, and business workflows.
- Collaborate with cross-functional teams to define technical requirements and AI solution architecture.
- Lead complex technology initiatives with enterprise-wide impact and influence AI engineering best practices.
- Evaluate emerging AI technologies and recommend innovative solutions to improve business outcomes.
- Develop scalable, secure, and maintainable AI applications following software engineering best practices.
- Participate in code reviews, architecture discussions, testing, debugging, and technical documentation.
- Mentor engineers and contribute to the development of AI engineering standards and best practices.
- Support MLOps initiatives to ensure reliable deployment, monitoring, and lifecycle management of AI models.
- 5+ years of Software Engineering or Machine Learning Engineering experience, or equivalent combination of education, military experience, training, and professional experience.
- Strong proficiency in Python development.
- Experience with machine learning frameworks such as PyTorch and TensorFlow.
- Hands-on experience building solutions with Large Language Models (LLMs), transformer architectures, and the Hugging Face ecosystem.
- Experience developing multi-agent AI systems utilizing session management, memory frameworks, and MCP tools.
- Knowledge of vector databases and Retrieval-Augmented Generation (RAG) architectures.
- Experience building and deploying scalable AI applications in enterprise environments.
- Strong understanding of software engineering principles, design patterns, and distributed systems.
- Excellent problem-solving, communication, and collaboration skills.
- Experience with cloud-based AI platforms including:
- AWS SageMaker
- Azure OpenAI
- Google Vertex AI
- Experience implementing MLOps practices, model deployment pipelines, and AI lifecycle management.
- Experience integrating AI solutions into web applications and enterprise platforms.
- Familiarity with containerization technologies and cloud-native architectures.
- Experience building multimodal AI applications.
- Understanding of AI governance, security, and responsible AI practices.
- Python
- PyTorch
- TensorFlow
- Hugging Face
- Large Language Models (LLMs)
- Prompt Engineering
- Retrieval-Augmented Generation (RAG)
- Vector Databases
- Semantic Search
- Multi-Agent Systems
- MCP (Model Context Protocol)
- AWS SageMaker
- Azure OpenAI
- Google Vertex AI
- MLOps
- REST APIs
- Cloud-Native Application Development
Vacancy posted 6 hours ago
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