Machine Learning Engineer / AI Engineer

Title: Machine Learning Engineer / AI Engineer Location: Charlotte, NC: Hybrid Rate: $70-$80/hr. on 1099 (MAX) Eligibility: No sponsorship consultants due to the nature of the work. Day to Day Job Duties • Partner with an assigned CIO organization as the primary GenAI engineering lead and translate business priorities into scalable AI solutions. • Design, develop, test, and deploy production-grade GenAI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic AI patterns. • Accelerate high-value use cases through hands-on engineering, rapid prototyping, reusable components, reference architectures, and production solutioning. • Drive adoption of enterprise AI platforms such as Azure AI, AWS Bedrock, and internal platforms; translate platform capabilities into practical implementation patterns. • Design secure RAG and agentic workflows using document ingestion, embeddings, vector search, orchestration frameworks, tools/APIs, enterprise data sources, and guardrails. • Ensure solutions meet enterprise architecture, security, privacy, responsible AI, governance, observability, and production support standards. • Deliver workshops, training, design reviews, and technical guidance on prompt engineering, RAG, orchestration, LLM application patterns, and platform usage. • Develop reusable documentation, playbooks, quick-start guides, sample code, and best practices while acting as liaison between centralized AI platform and CIO engineering teams. Basic Qualifications • Minimum 8 years of overall software engineering, data engineering, machine learning engineering, or AI solution development experience in enterprise environments. • Minimum 5 years of hands-on experience designing and delivering machine learning, AI, or data-driven applications using Python and modern software engineering practices. • Minimum 3 years of hands-on experience building Generative AI / LLM applications, including prompt engineering, model integration, evaluation, and production deployment. • Minimum 3 years of experience designing and implementing RAG solutions using embeddings, vector search/databases, document processing, retrieval techniques, and grounding patterns. • Minimum 2 years of experience developing agentic AI or multi-step AI workflows using orchestration frameworks, tool/function calling, APIs, and enterprise integrations. • Minimum 3 years of experience with cloud AI/ML platforms, including Azure AI / Azure OpenAI, AWS Bedrock, or comparable enterprise AI services. • Minimum 3 years of experience with cloud-native engineering, CI/CD, containers, observability, security controls, and production deployment practices. • Minimum 2 years of experience in technical enablement, architecture, consulting, or engineering leadership, including workshops, mentoring, reusable guidance, and application of responsible AI/governance requirements. Travel: Travel as required based on business and CIO-aligned engagement needs. Degree: Bachelor’s degree in computer science, Engineering, Data Science, Artificial Intelligence, Machine Learning, or equivalent work experience. Nice to Have • Experience with LangChain, LangGraph, Semantic Kernel, LlamaIndex, AutoGen, or comparable LLM / agent orchestration frameworks. • Experience with vector technologies such as Azure AI Search, OpenSearch, Pinecone, pgvector, FAISS, or equivalent platforms. • Experience with LLMOps / MLOps, prompt and model evaluation, AI observability, guardrails, red-teaming, and production quality measurement. • Experience building reusable GenAI accelerators/reference architectures or delivering AI solutions in highly regulated enterprise environments such as financial services.