Top AI & Machine Learning Jobs in Charlotte, NC

Designs and accelerates AI-enabled capabilities across ITSM products, initially Incident, Change, and Problem Management. Translates workflows into practical solution architectures, evaluates automation and AI patterns, prototypes capabilities, and guides implementation. Partners with product, engineering, architecture, platform, and governance teams to ensure scalable, supportable, risk-aligned solutions. Coaches teams on AI design tradeoffs, influences strategy, and establishes repeatable patterns and success measures across multiple ITSM offerings.

Leads enterprise AI solutions strategy, product management, governance, adoption, and roadmap execution for employee-facing productivity and automation platforms. Oversees AI initiatives involving machine learning, natural language processing, robotic process automation, and generative AI while ensuring security, compliance, responsible AI, and measurable business outcomes. Manages teams, budgets, executive stakeholders, market strategy, talent development, and enterprise technology transformation in a regulated environment.

Participate in a 12-month quantitative analytics program with two rotations focused on applied AI, machine learning, generative AI, agentic systems, and advanced modeling. Develop, fine-tune, evaluate, optimize, and deploy language models and scalable AI pipelines. Work with LLM agents, RAG, distributed training, model monitoring, and quantitative validation while collaborating with technical and business teams on financial-services challenges.

Ten-week Wells Fargo internship for master’s students applying quantitative analytics, machine learning, and generative AI to financial-services challenges. Interns may develop AI advisors, LLM assistants, agentic and multi-agent systems, RAG platforms, model-training pipelines, and scalable machine-learning solutions. Responsibilities include model evaluation, optimization, deployment, statistical validation, and risk-aware data analysis. The program includes mentorship, technical training, networking, and strategic project work in Charlotte, North Carolina.

Ten-week quantitative analytics internship focused on applying artificial intelligence, machine learning, generative AI, and statistical techniques to financial-services challenges. Interns may develop LLMs, RAG systems, multi-agent architectures, AI assistants, decision-support tools, and scalable machine-learning solutions. Responsibilities include model training, evaluation, optimization, deployment, quantitative validation, and collaboration with business and technical teams. The role includes mentorship, training, networking, and potential consideration for full-time employment.

Participate in a 12-month quantitative analytics development program focused on applied AI and machine learning. Build and deploy LLM-powered agents, multi-agent systems, RAG applications, model training pipelines, and enterprise decision-support solutions. Projects include model evaluation, optimization, responsible AI, distributed training, and production monitoring. Collaborate with technical and business teams across two six-month rotations, applying quantitative methods to improve customer experience, risk management, productivity, and operational efficiency.

Twelve-month rotational program for PhD-level quantitative analytics candidates. Participants develop and validate statistical, machine learning, and AI models for credit risk, financial crime, operational loss, customer experience, and portfolio forecasting. The role involves large-scale data analysis, model deployment, data engineering, collaboration with senior leaders, and risk-focused decision support. Successful participants transition into a full-time role after completing two six-month rotations.

Ten-week quantitative analytics internship applying statistical modeling, machine learning, artificial intelligence, and data analysis to financial risk challenges. Interns may forecast credit portfolio losses and revenue, develop credit scorecards, detect money laundering, predict operational losses, and validate quantitative models. The program includes mentorship, technical training, leadership exposure, and professional development in a collaborative environment.

Participate in a 12-month quantitative analytics development program with two rotations. Develop, evaluate, validate, and deploy statistical, machine learning, and AI models supporting credit risk, financial crime, operational risk, customer experience, and business decisioning. Analyze large datasets, build scalable solutions, collaborate with quantitative professionals and senior leaders, and translate analytical findings into actionable business insights.

PhD-level summer intern will apply advanced analytics, AI/ML, and quantitative techniques to develop, validate, and enhance pricing, simulation, and benchmark models for capital markets. Responsibilities include model development, performance monitoring, statistical validation, reporting metrics, and collaborating with quantitative teams to deliver scalable, risk-aware solutions over a 10-week program.