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Machine Learning Engineer / AI Engineer (GenAI | RAG | Agentic AI | Python)
United Software Group
2 hours ago
Full-time
On-site
Charlotte, North Carolina, United States
Title:
Machine Learning Engineer / AI Engineer (GenAI | RAG | Agentic AI | Python)
Charlotte, NC
About the Role
We're looking for a senior, hands-on AI Engineer to speed up adoption of enterprise AI platforms across lines of business at a large financial services client. You'll act as the GenAI engineering lead for an assigned technology organization, turning business priorities into production-grade solutions built on LLMs, RAG, and agentic AI. You'll also deliver reusable patterns, training, and technical guidance, so teams can build faster, securely, and in line with responsible AI standards.
Location:
Charlotte, NC Start Date:
October 26, 2026 Duration:
Contract through December 25, 2026
Key Responsibilities Partner with an assigned technology organization as the primary GenAI engineering lead, translating business priorities into scalable AI solutions Design, build, test, and deploy production-grade GenAI solutions using LLMs, RAG, and agentic AI patterns Speed up high-value use cases through rapid prototyping, reusable components, and reference architectures Drive adoption of enterprise AI platforms such as Azure AI and AWS Bedrock, turning platform capabilities into practical implementation patterns Design secure RAG and agentic workflows with document ingestion, embeddings, vector search, orchestration frameworks, tools/APIs, and guardrails Make sure solutions meet security, privacy, responsible AI, governance, observability, and production support standards Run workshops, training, and design reviews on prompt engineering, RAG, orchestration, and LLM application patterns Create playbooks, quick-start guides, sample code, and best practices, and act as the link between the central AI platform team and engineering teams Required Skills
8+ years in software engineering, data engineering, ML engineering, or AI solution development in enterprise environments 5+ years building ML, AI, or data-driven applications in Python 3+ years building GenAI/LLM applications, including prompt engineering, model integration, evaluation, and production deployment 3+ years designing RAG solutions with embeddings, vector search/databases, document processing, and grounding patterns 2+ years building agentic AI or multi-step AI workflows with orchestration frameworks, tool/function calling, and enterprise integrations 3+ years with cloud AI/ML platforms (Azure AI / Azure OpenAI, AWS Bedrock, or comparable) 3+ years in cloud-native engineering: CI/CD, containers, observability, security controls, and production deployment 2+ years in technical enablement, architecture, consulting, or engineering leadership (workshops, mentoring, responsible AI/governance) Bachelor's degree in Computer Science, Engineering, Data Science, AI, ML, or equivalent experience Preferred Skills
LangChain, LangGraph, Semantic Kernel, LlamaIndex, AutoGen, or similar orchestration frameworks Vector technologies such as Azure AI Search, OpenSearch, Pinecone, pgvector, or FAISS LLMOps/MLOps, prompt and model evaluation, AI observability, guardrails, and red-teaming Reusable GenAI accelerators or AI delivery in regulated environments such as financial services
Machine Learning Engineer / AI Engineer (GenAI | RAG | Agentic AI | Python)
Charlotte, NC
About the Role
We're looking for a senior, hands-on AI Engineer to speed up adoption of enterprise AI platforms across lines of business at a large financial services client. You'll act as the GenAI engineering lead for an assigned technology organization, turning business priorities into production-grade solutions built on LLMs, RAG, and agentic AI. You'll also deliver reusable patterns, training, and technical guidance, so teams can build faster, securely, and in line with responsible AI standards.
Location:
Charlotte, NC Start Date:
October 26, 2026 Duration:
Contract through December 25, 2026
Key Responsibilities Partner with an assigned technology organization as the primary GenAI engineering lead, translating business priorities into scalable AI solutions Design, build, test, and deploy production-grade GenAI solutions using LLMs, RAG, and agentic AI patterns Speed up high-value use cases through rapid prototyping, reusable components, and reference architectures Drive adoption of enterprise AI platforms such as Azure AI and AWS Bedrock, turning platform capabilities into practical implementation patterns Design secure RAG and agentic workflows with document ingestion, embeddings, vector search, orchestration frameworks, tools/APIs, and guardrails Make sure solutions meet security, privacy, responsible AI, governance, observability, and production support standards Run workshops, training, and design reviews on prompt engineering, RAG, orchestration, and LLM application patterns Create playbooks, quick-start guides, sample code, and best practices, and act as the link between the central AI platform team and engineering teams Required Skills
8+ years in software engineering, data engineering, ML engineering, or AI solution development in enterprise environments 5+ years building ML, AI, or data-driven applications in Python 3+ years building GenAI/LLM applications, including prompt engineering, model integration, evaluation, and production deployment 3+ years designing RAG solutions with embeddings, vector search/databases, document processing, and grounding patterns 2+ years building agentic AI or multi-step AI workflows with orchestration frameworks, tool/function calling, and enterprise integrations 3+ years with cloud AI/ML platforms (Azure AI / Azure OpenAI, AWS Bedrock, or comparable) 3+ years in cloud-native engineering: CI/CD, containers, observability, security controls, and production deployment 2+ years in technical enablement, architecture, consulting, or engineering leadership (workshops, mentoring, responsible AI/governance) Bachelor's degree in Computer Science, Engineering, Data Science, AI, ML, or equivalent experience Preferred Skills
LangChain, LangGraph, Semantic Kernel, LlamaIndex, AutoGen, or similar orchestration frameworks Vector technologies such as Azure AI Search, OpenSearch, Pinecone, pgvector, or FAISS LLMOps/MLOps, prompt and model evaluation, AI observability, guardrails, and red-teaming Reusable GenAI accelerators or AI delivery in regulated environments such as financial services