AI Engineer

• Define and Maintain the Wind Engineering AI Governance Framework
• Establish and Enforce Responsible AI Policies
• Oversee Risk Management, Validation, and Auditability
• Govern Cybersecurity, IP Protection, and Data Privacy for AI
• Provide Governance Approval and Oversight for AI Deployments
• Partner with Legal, Security, Privacy, and Compliance Teams

Governance & Responsible AI Leader

• Ship and debug code on a live, real-time voice pipeline where latency and correctness are user-facing
• Design control systems around LLMs: guardrails, budgets, watchdogs, safe fallbacks
• Build and operate LLM evaluation and batch-analysis pipelines
• Own traditional ML workflows from data to scheduled production inference
• Trace production issues from a metric anomaly to root cause, including building the evidence when the cause is a vendor

Senior Software Engineer – Applied AI

• Own stakeholder relationships across Medbridge’s executive team, working with them to find the problems and AI enabled solutions that can expedite, or remove, the current blockers in the business.
• Design, build, and ship AI-powered features across our internal teams, from the user interface to the backend services and AI workflows behind it.
• Build full-stack applications using AI-native engineering workflows and tools such as Claude, Cursor, Codex, and OpenAI and Anthropic APIs.
• Develop APIs, backend services, and automation pipelines that support intelligent product capabilities and production-ready deployments.
• Apply software engineering, DevOps, and MLOps best practices to the testing, deployment, monitoring, evaluation, and optimization of AI solutions.
• Partner closely with our Product and Engineering teams to turn ambiguous goals into scalable internal products.
• Use AI tooling throughout your workflow (coding, code review, testing, and planning) and help push the team’s adoption forward.
• Research emerging AI technologies, LLM implementation patterns, and modern product engineering frameworks, and bring what works back to the team.
• Instrument what you ship and use usage data and evidence to decide what to build, fix, or kill next.

Product Engineer – AI

• Design, develop, and deploy machine learning models and AI-powered applications
• Collaborate with cross-functional teams to identify and implement AI/ML solutions for client needs
• Build and maintain data pipelines, model training workflows, and inference systems
• Evaluate and integrate large language models (LLMs), generative AI tools, and emerging AI frameworks
• Monitor model performance, conduct testing, and iterate on improvements
• Document technical architectures, methodologies, and best practices
• Stay current with AI/ML research and translate advancements into practical applications

• Define and evolve enterprise AI architecture, standards, reference models and implementation guidelines to support consistent solution delivery across business functions
• Align AI initiatives to enterprise strategies, roadmaps, and business priorities
• Identify and prioritize AI opportunities that drive student success and operational efficiency
• Design scalable AI platforms and solutions across data, application, and technology domains
• Provide guidance on how AI, ML, and automation will improve current and future-state architectures
• Lead design and implementation of AI-powered solutions (e.g., LLMs, agents, automation, and analytics)
• Integrate AI capabilities with enterprise applications and data platforms
• Guide evaluation and selection of AI/ML models, frameworks, and vendors
• Establish reusable patterns, APIs, and integration approaches
• Establish governance practices covering model oversight, responsible AI usage, security controls, compliance expectations, and lifecycle management
• Establish AI governance, including responsible AI, security, privacy, and compliance (FERPA, HIPAA, GDPR)
• Establish lifecycle management practices (MLOps/LLMOps, model monitoring, drift detection, retraining)
• Implement performance, bias, and risk monitoring frameworks
• Ensure secure AI architecture (e.g., data protection, isolation, adversarial defense)
• Partner with business, product, data, and engineering teams to provide AI solutions
• Provide architectural guidance to architects and delivery teams
• Facilitate workshops, POCs, demos and AI adoption projects identifying viable opportunities where AI can be a positive differentiator
• Mentor teams on AI best practices, patterns, and frameworks

Enterprise AI Architect

• Build and maintain shared infrastructure: secure web app hosting infrastructure, MCP/API standards, secret/key management, model selection, guardrails, and a centralized pattern library that all builders across the organization can pull from.
• Own the review, tracking, and advisory process for all corporate AI and automation builds — MCP servers and tool configurations, Claude Skills, API integrations, and agentic workflows — before deployment.
• Partner directly with AI specialists across departments to identify, scope, and build AI and automation solutions that meet high standards for cost, security, and scalability security before they ship.
• Define and maintain the secure intake path for AI and automation requests across the organization, replacing ad-hoc builds and shadow IT with a governed, cost-effective, and scalable process.
• Contribute to the organization’s AI cost efficiency by monitoring, alerting, and setting thresholds on AI spend across usage-based tools and platforms.

AI & Automations Lead

LLM Engineer

• Design, build, and maintain scalable ML infrastructure and pipelines supporting model training, deployment, monitoring, governance, and lifecycle management.
• Develop and optimize CI/CD pipelines for machine learning and AI workloads across development, staging, and production environments.
• Build reusable ML platform capabilities including feature stores, model registries, experimentation frameworks, artifact management, and deployment automation.
• Implement scalable orchestration and workflow solutions for batch and real-time ML inference workloads.
• Create robust monitoring systems to measure model performance, detect model drift, monitor data quality, and ensure production reliability.
• Develop automation tools and self-service capabilities to improve the efficiency, scalability, and reliability of MLOps processes.
• Collaborate with Data Scientists and Software Engineers to streamline the ML lifecycle from experimentation through enterprise production deployment.
• Apply software engineering best practices to AI/ML systems including testing, observability, resiliency, security, versioning, and infrastructure-as-code.
• Identify gaps and improvement opportunities within the organization’s ML platform ecosystem and architect scalable solutions to address them.
• Support enterprise AI governance, compliance, auditability, and model risk management requirements.
• Ensure platform scalability, reliability, security, and operational excellence across AI/ML systems.
• Lead the architecture, design, and deployment of enterprise Generative AI solutions leveraging LLMs, foundation models, and agentic AI systems.
• Design and implement Retrieval-Augmented Generation (RAG) pipelines using vector databases, embeddings, semantic search, reranking, and retrieval optimization strategies.
• Build scalable LLM orchestration frameworks using technologies such as LangChain, LlamaIndex, Semantic Kernel, or equivalent frameworks.
• Develop advanced prompt engineering strategies, prompt chaining, context management, and agent workflows to improve LLM accuracy and reliability.
• Evaluate and implement fine-tuning, parameter-efficient tuning, and prompt-based optimization approaches for domain-specific use cases.
• Build AI evaluation and benchmarking frameworks to measure hallucination rates, response quality, grounding accuracy, toxicity, bias, latency, and business performance metrics.
• Implement AI safety guardrails, governance controls, content filtering, and responsible AI practices for enterprise healthcare environments.
• Design scalable GenAI APIs and microservices supporting high-throughput enterprise AI applications.
• Optimize GenAI systems for cost, latency, throughput, and inference performance across cloud and hybrid environments.
• Integrate enterprise data sources, healthcare systems, and knowledge repositories into secure GenAI workflows.
• Research and evaluate emerging GenAI technologies, open-source frameworks, and foundation models to drive innovation and continuous improvement.
• Develop architecture diagrams, technical roadmaps, implementation strategies, and executive-level documentation for enterprise AI initiatives.
• Collaborate with cybersecurity, compliance, and infrastructure teams to ensure secure and compliant deployment of GenAI solutions involving PHI and sensitive healthcare data.
• Contribute to the development of AI platform standards, reusable GenAI accelerators, templates, and engineering best practices.

Senior MLOps, Generative AI Engineer

• Own and develop enterprise accounts across the Southeast (GA, FL, TN, NC, SC, AL)
• Build 50%+ of pipeline through cold outreach, events, networking, and partner collaboration
• Manage complex enterprise sales cycles ($75K-$200K ACV, 3-9 months) with multiple stakeholders
• Sell to CIOs, COOs, CFOs, and operations leaders at manufacturers, financial institutions, and enterprise companies
• Run discovery, demos, POCs, business case development, and contract negotiation
• Partner with channel ecosystem to co-develop and accelerate opportunities
• Maintain Salesforce CRM hygiene, pipeline accuracy, and quarterly forecasting
• Travel across Southeast territory 20-40%

Enterprise Sales Executive – AI Platform

• Design, develop, and deploy enterprise AI applications, AI agents, copilots, and intelligent workflow solutions across manufacturing and corporate domains.
• Build and maintain AI orchestration pipelines utilizing LLMs, retrieval-augmented generation (RAG), vector search, prompt frameworks, and agent-based architectures.
• Develop scalable AI integration patterns between enterprise platforms such as Snowflake, Salesforce, ERP systems, SCADA/OT systems, document repositories, and collaboration platforms.
• Collaborate with data scientists and business stakeholders to operationalize machine learning and generative AI solutions into production environments.
• Design APIs, middleware, and integration services supporting AI-driven automation and cross platform communication.
• Develop and optimize semantic search, enterprise knowledge retrieval, and conversational AI capabilities.
• Support the implementation of AI governance, security, observability, model evaluation, and human-in-the-loop approval processes.
• Drive enterprise AI architecture strategy and standards, partnering across teams to define approaches for AI platforms, orchestration, deployment, and lifecycle management.

• Define and own end-to-end enterprise architecture for a Microsoft-based customer engagement and CCaaS platform
• Establish target-state architecture across sales, service, channels, integrations, data, and security
• Develop architecture principles, standards, patterns, and governance guardrails
• Architect solutions across Dynamics 365 Sales, Customer Service, and Omnichannel
• Design end-to-end customer and seller journeys including Lead-to-opportunity, Opportunity-to-close, Account management, Case management and resolution
• Define architecture for: Sales automation, Case management and service workflows, Routing, queues, workstreams, and skills-based distribution
• Architect solutions leveraging Power Platform, Microsoft Teams and Microsoft 365
• Define patterns for integration with third-party and ISV platforms
• Define AI governance including prompt design, security, and responsible AI controls
• Organize and oversee architecture authority across programs and workstreams.

Machine Learning Engineer jobs in South Carolina

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AI Engineer

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