Artificial Intelligence Jobs in Columbia, SC

Lead design and implementation of Microsoft Dynamics 365 solutions, supervise and mentor teams, manage project delivery, translate complex requirements into technical designs, handle data migrations and integrations, and build strong client relationships while improving service delivery.

Lead Microsoft Dynamics 365 CRM implementations, architect solutions, manage project lifecycles and client accounts, mentor teams, ensure quality and post-go-live support, and drive innovation and continuous improvement.

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

Leads the engineering organization responsible for developing and executing fleet-scale nuclear power plants. Builds multidisciplinary teams, establishes engineering governance and technical controls, oversees lifecycle engineering from design through operations, and manages reactor technology providers, architect-engineers, EPCs, suppliers, utilities, and regulators. Drives constructability, operability, standardization, configuration control, nuclear safety, regulatory compliance, and AI-enabled engineering productivity across repeatable nuclear projects.

Lead and coach teams to design, develop, and deliver production-grade AI and data solutions for clients. Manage client accounts, ensure quality and timelines, analyze complex datasets, implement best practices, and drive continuous improvement while fostering client relationships and technical excellence.

Lead large Microsoft Dynamics 365 (CRM/CE) implementations as a Senior Manager: engage senior clients, design functional solutions, oversee delivery lifecycle, coach teams, refine processes, and ensure operational excellence and adherence to firm standards.

Lead design and implementation of data architecture for healthcare systems, influence stakeholders, mentor teams, ensure regulatory compliance, enable data integration and transformation, and apply AI techniques to improve healthcare operations and reporting.

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

Lead design, build, and operation of scalable, secure AI-enabled platforms; mentor engineers; ensure reliability, security, and delivery of conversational AI and cloud-native solutions; collaborate cross-functionally to translate use cases into platform capabilities.

Lead design and delivery of advanced data and ML solutions, performing exploratory analysis, statistical modeling, and visualization to inform strategy. Manage and coach teams, collaborate with clients to validate outcomes, and improve analytics processes and data quality to drive business decisions.

Leads the financial and operational internal audit plan, directing concurrent audits, supervising audit staff, reviewing audit quality and findings, and advising senior management. Oversees risk assessments, internal controls, audit reporting, strategic improvement initiatives, and quality programs. Evaluates and deploys AI-enabled audit tools, prepares executive and committee presentations, manages audit issues, and coordinates with enterprise risk, compliance, and information security functions.

Lead end-to-end delivery of AI and ML services within Managed Services. Manage project planning, budgeting, execution, and quality. Mentor and develop team members, cultivate client relationships, implement model monitoring and operationalization practices, and collaborate with architects and engineers to ensure compliant, reusable AI solutions.

Lead administrative, academic, and operational management of a new grant-funded Master of Science in Applied Computing: coordinate curriculum, faculty, research, admissions, budgeting, grant compliance, student support, and external partnerships to grow and sustain the program.

Serve as liaison between business and centralized AI teams to identify, prioritize, and implement AI and automation opportunities. Analyze workflows, manage projects end-to-end, coordinate stakeholders, and drive change management, training, and adoption to deliver measurable operational efficiencies and improved client and employee experiences.

Design and enforce secure AI architecture and controls across the enterprise. Partner with Architecture, Delivery, Privacy, IAM, Vendor Risk, CSOC, Legal and business owners to translate AI risk, security, privacy and compliance requirements into reference architectures, implementation guidance, and reviews. Provide technical oversight for complex projects, mentor staff, evaluate vendor solutions, author whitepapers, and support RFPs and audits to ensure AI solutions are secure and compliant.

As an AI/ML Data Contributor, you perform tasks like data labeling and content evaluation to enhance machine learning models, ensuring quality and adhering to project timelines.

The AI/ML Data Contributor will perform data labeling, content evaluation, and user testing to improve machine learning models, participating in remote and on-site tasks.

AI/ML Data Contributors will perform tasks like data labeling and evaluation to enhance machine learning models, both remotely and on-site.

As an AI/ML Data Contributor, you'll perform data labeling and evaluation tasks to improve machine learning models, working flexibly on various projects.

Research, design, and implement foundation models for machine-generated and graph data; develop distributed training and inference pipelines; collaborate with engineering and product teams to productionize models; evaluate new AI/ML techniques and drive MLOps, monitoring, and model optimization to improve scalability and reliability.