Artificial Intelligence

• Lead discovery workshops to clarify business objectives, constraints, and measurable success criteria for AI/ML initiatives
• Translate requirements into end-to-end target-state architectures across data, ML/AI, application integration, security, and governance
• Define pragmatic tradeoffs across accuracy, latency, cost, reliability, privacy, and risk—and communicate decisions to exec and engineering audiences
• Design the data + model lifecycle (pipelines, training/finetuning, serving, monitoring, drift detection, retraining) and the required MLOps/LLMOps foundations
• Establish integration patterns with enterprise systems (APIs/events/workflows, IAM, observability) so solutions are operable and supportable in production
• Partner with security, privacy, and risk teams to embed controls (access, auditability, data handling, responsible AI) into solution designs
• Produce core delivery artifacts (architecture diagrams, reference patterns, implementation roadmap, runbooks) and drive architecture reviews
• Mentor teams and build reusable assets/accelerators (reference architectures, templates, evaluation scorecards) to scale repeatable delivery quality

AI-ML Director

• Facilitate development of AI strategies and roadmaps aligned to agency mission, priorities, and operational realities
• Advise leadership on AI investment planning, use case prioritization, and value realization
• Lead stakeholder engagements to assess organizational readiness, data maturity, and change considerations
• Translate complex AI concepts into clear, actionable recommendations for business and non-technical audiences
• Identify and define high-impact AI use cases across programs, operations, and service delivery
• Support evaluation and selection of AI technologies and solutions, including vendor and platform considerations
• Develop frameworks for responsible AI adoption, including governance, risk, and oversight
• Facilitate workshops and working sessions to build alignment across business, IT, and data teams
• Support early-stage implementation planning, including operating model, workforce impacts, and adoption strategy

Senior Consultant – AI Strategist

• Conduct training sessions for SNHU community members to ensure use of AI tools and technologies
• Provide support in the form of hands-on guidance and consultation
• Help optimize existing AI-driven processes to improve operational and educational efficiency
• Collaborate with cross-functional teams to support AI Proofs of Concept, projects, and product exploration
• Assist in brainstorming and planning for faculty and staff ideas related to AI integration
• Develop training materials, guides, and resources to support AI tool adoption across the institution
• Attendance, punctuality, and reliability are essential functions of this role
• Other responsibilities as assigned

AI Integration Specialist

• Work with Autodesk’s largest customers to define innovative Building design, engineering, and management strategies and solutions to ensure the greatest possible value from their Autodesk investment
• Work with Autodesk account teams to define Customer Success Plans and customer engagements to deliver those strategies and solutions
• Work with the Autodesk Consulting Project Management team to ensure exceptional delivery against the Customer Success Plan
• Shape the Autodesk Consulting service offering and how it is delivered
• Interface with thought leaders within Autodesk, and beyond, to support and shape the Autodesk product offering
• Be a trusted business partner to customers and internal Autodesk teams
• Develop and deliver truly innovative business and technology solutions that have a strategic impact on Autodesk customers
• Help Autodesk customers drive transformational change by leveraging proven change management methodologies
• Lead facilitated workshops with Autodesk customers to identify solution opportunities and drive alignment on solution paths
• Lead or assist in the creation of high-quality design, engineering, and operational management deliverables for customers
• Define and track the value of solutions delivered to customers, and help document the impact of customer investment in Autodesk Consulting
• Lead and/or participate in the development around new and expanded offerings as part of a comprehensive consulting delivery portfolio
• Build the Autodesk Consulting brand by demonstrating thought leadership within the industry
• Lead project delivery efforts, ensuring team members perform to the highest level of quality and professionalism
• Mentor junior staff in successful customer engagement techniques including change management, strategic and critical thinking, workshop facilitation, and effective communication
• Advise on project estimation and participate in business development activities

Principal Business Consultant, Buildings – Data / AI Strategy and Readiness

• Serve as EnerSys’ executive leader and single point of accountability for enterprise AI strategy, execution, and outcomes.
• Translate enterprise and business strategies into a clear, multi‑year AI vision and roadmap.
• Advise the CIO, ELT, and Board on AI trends, risks, opportunities, and competitive implications.
• Represent EnerSys externally with strategic AI partners, technology providers, and industry forums.
• Design and implement the enterprise AI operating model, including governance forums and funding processes.
• Establish enterprise standards for responsible AI, data usage, security, privacy, and risk management.
• Own AI investment governance, ensuring alignment with capital planning and transformation funding.
• Own the global AI use case portfolio and prioritization framework.
• Define, track, and report enterprise AI value metrics.
• Orchestrate cross-functional delivery across IT, data, and business units.
• Partner with HR to lead enterprise-wide change management and AI literacy initiatives.
• Build, lead, and develop a high performing enterprise AI organization with clear roles.

VP, Enterprise AI Enablement

• AI & Data Science Collaboration: Partner with GDIA Data Scientists to extract and analyze multi-source data for early trend detection and root-cause identification.
• Proactive System Leadership: Drive the innovation of quality processes by scaling "Field Intelligence" and "Command Center" models.
• Lead Strategic Action Plans: Develop robust business cases and prioritized action plans for top emerging issues (e.g., software deployment failures, hardware defects).
• The FCSD Product Uptime Manager Liaison acts as the ultimate bridge between advanced data science (GDIA), core Product Engineering, Quality, and Service Engineering Operations (SEO).
• Proactive Uptime Triage: Utilize statistical analysis and AI-driven "Service Complexity Predictors" to identify at-risk repairs.
• Dashboard & KPI Ownership: Develop and maintain high-impact dashboards (Google Looker/Streamlit).
• Executive Storytelling: Distill complex data patterns and AI signals into concise "One-Pagers" for FCSD, Quality, and Engineering leadership.
• Continuous Improvement: Drive PDCA (Plan-Do-Check-Act) cycles to evaluate the effectiveness of corrective actions on repair speed and first-time fix rates.

Manager, Uptime AI

• Conduct research and/or experiments to collect required data and validate research ideas
• Apply AI/ML, data science, and computational methods to engineering and research problems
• Support development of methods, models, tools, or workflows that accelerate vehicle development and research outcomes
• Assist with analysis of large, complex datasets to generate insights and improve prediction, automation, or decision-making
• Collaborate with technical teams to evaluate emerging technologies and identify opportunities for application in automotive research
• Document and present results; depending on the assignment, work may lead to external publications and/or intellectual property
• Develop and execute specific research plans, including concept generation, development, implementation, and validation
• Collaborate effectively with peers, management, operations groups, and outside organizations

Fall Intern – Research & Development, AI/ML

• Build and maintain features for our agentic applications, writing clean, tested, and well-documented code under the guidance of senior engineers.
• Assist in developing and debugging components of our agentic workflows, learning how stateful agents are structured, deployed, and monitored.
• Write scripts and small utilities that improve developer experience, automate repetitive tasks, and support the team's CI/CD pipelines.
• Contribute to unit and integration tests, help triage bugs, and participate in code reviews to learn and uphold team standards.
• Keep technical documentation current as features evolve, making it easier for the whole team to move quickly.

AI Orchestration Engineer Intern

• Own end-to-end governance of enterprise Quality platforms.
• Define and enforce standards, workflows, and operating models across all Quality tools.
• Establish governance for tool lifecycle management.
• Lead strategy, deployment, and value realization of AI-driven Quality solutions.
• Identify and scale high-impact AI/automation use cases.
• Ensure AI solutions are trusted, explainable, and aligned with business and regulatory requirements.
• Own integration of Quality systems with enterprise data and analytics platforms.
• Define data models, reporting standards, and metrics governance.
• Lead alignment across Engineering, Software, Safety, IT, and Global Quality.
• Simplify and standardize Quality processes.

Quality Systems Manager – Digital Quality Platforms, AI

• Lead the strategic integration of next-generation digital tools, advanced analytics, and intelligent systems across Ford’s Global Product Development System (GPDS).
• Optimize engineering workflows, accelerate development cycles, and drive product excellence.
• Oversee the architectural integration of AI across the entire Ford product creation lifecycle.
• Recommend investments, and design and execute systems to optimize engineering workflows.
• Work across active and future vehicle programs to deploy next-generation tools.

Chief Engineer, AI Product Creation

• Set the multi-year strategy for ML, data, and BI
• Build and scale the Data Science / AI, Data Engineering, and BI Engineering organization
• Manage managers and tech leads
• Run performance management, career development, and retention
• Operate as a peer to VP-level leaders across various functions
• Own the architecture, deployment, and operation of the production ML

VP of AI, ML and Data Strategy

• Bring a consultative approach to every engagement, taking the time to understand a team's workflows, challenges, and goals before identifying where AI can make a real difference
• Partner with leaders across all major functions to surface workflow friction, knowledge gaps, and high-value opportunities where AI tools or training can make a meaningful difference
• Own and drive the enterprise AI enablement roadmap, scoping, prioritizing, and executing a portfolio of initiatives from discovery through adoption and measurement
• Define and maintain company-wide AI policy and guardrails, ensuring Crain is adopting AI in a way that is responsible, secure, and aligned with our values and obligations
• Lead the effort to capture and centralize Crain's institutional knowledge (decisions, processes, data, and expertise) into AI-powered knowledge bases that make the whole organization smarter: HR resources, customer research, sales and marketing materials, process documentation, and more
• Partner with IT Services on the company-wide rollout of Claude Enterprise, driving adoption and ensuring teams are set up to get real value from the platform within our Microsoft 365 environment
• Identify and implement agentic workflows and automation that reduce manual overhead across key business functions
• Build a structured approach to connecting Crain's core data sources (Naviga, Salesforce, ArcXP, Smartsheet, Fireflies, AWS, and others) as inputs into a coherent, AI-accessible model of how the business operates
• Implement and manage a company-wide AI micro-learning platform, rolling it out in deliberate phases starting where it will have the most impact, tracking adoption and outcomes at each stage to learn, iterate, and expand intelligently across the organization
• Stay close to advancements in the AI space, continuously evaluating new tools, models, and capabilities; find regular, structured ways to surface and communicate relevant opportunities to stakeholders across Crain so we're always making informed decisions about what to explore, pilot, or adopt
• Maintain a clear focus on cost efficiency and ROI across all AI initiatives; every tool evaluated, every workflow built, and every platform adopted should be held to a standard of delivering measurable value for the business

Director, Enterprise AI Enablement

• Architect internal productivity revolution and analyze workflows.
• Rapidly prototype and refine agentic solutions.
• Help build and deploy production grade AI agents.
• Integrate AI agents with enterprise resources.
• Define and track productivity & ROI.
• Stay at the forefront of AI developments in the media industry.

Director, Applied AI – Strategy

• Lead the development and deployment of advanced AI and machine learning solutions that accelerate GM’s research, engineering, and product development capabilities.
• Build, validate, deploy, and scale AI/ML models for high-impact engineering, research, and product development applications.
• Lead technical work across a broad set of AI domains, including classical machine learning, deep learning, GenAI, Large Language Models, multimodal AI, and physics-based AI.
• Translate business and engineering problems into practical AI/ML solution strategies and executable technical plans.
• Serve as a technical authority on model development, deployment architectures, data strategy, evaluation methods, and responsible AI practices.
• Provide strategic guidance to leadership on AI technology choices, investment areas, roadmap priorities, and capability development.
• Communicate complex technical concepts, tradeoffs, and recommendations clearly and effectively to executive and non-technical audiences.
• Partner closely with engineering, product, and software teams to transition AI concepts into deployed solutions.
• Evaluate emerging AI technologies and identify where they can strengthen GM’s technical portfolio and long-term competitive advantage.
• Mentor technical teams and influence best practices in applied AI/ML development, experimentation, deployment, and governance.
• Contribute to thought leadership through technical reviews, publications, invention disclosures, and cross-functional collaboration.

AI/ML Staff Researcher

• Serve as a trusted advisor to State Health and Medicaid leaders on developing an AI strategy that covers: Core enterprise AI capabilities (data readiness, governance, model lifecycle management, tooling)
• Lead engagements from strategy through implementation, with a focus on outcomes, adoption, and sustainable operations.
• Translate complex health and Medicaid business challenges into actionable AI/ML and analytics solutions using platforms such as Azure, AWS, GCP, Databricks, Snowflake, and related services.
• Partner with IT, program, privacy, security, legal, and procurement stakeholders to support delivery that aligns to agency policies and applicable regulations.
• Facilitate use case discovery and prioritization workshops with stakeholders across agency divisions.
• Define an AI use case portfolio and roadmap that balances: Quick-win productivity use cases, Program modernization and longer-term transformation, Foundational capabilities (data, governance, operating model) needed to scale.
• Create pragmatic adoption plans including workforce readiness, change management, training, and communications.
• Guide solution design and implementation for AI and GenAI capabilities, including: Decision support and predictive analytics, Intelligent document processing, summarization, and search, Customer service and contact center augmentation, Caseworker support tools and workflow automation, Program integrity and anomaly detection.
• Lead and advise on production readiness topics such as model monitoring, drift, validation, human-in-the-loop workflows, and incident response procedures.
• Apply Medicaid and state health data knowledge across domains such as: Claims and encounters, eligibility and enrollment, provider data, managed care, long-term services and supports, pharmacy, behavioral health, and social determinants.
• Support data integration and interoperability approaches using standards and frameworks such as FHIR, HL7, and related health data exchange patterns where relevant.
• Incorporate responsible AI practices, transparency, auditability, and privacy-preserving approaches into delivery.
• Drive growth through capture support, proposal development, orals preparation, and strategic pursuits focused on State Health and Medicaid priorities.

AI Strategy Associate Director

• Define and evolve target-state data platform and data services architecture aligned to business strategy and modernization goals
• Establish enterprise standards and reference architectures for data as a service, event-driven architecture, and API-based data access
• Drive adoption of data as a strategic asset, enabling operational, analytical, and intelligent system use cases across the enterprise
• Architect scalable data platforms that support both traditional workloads and emerging AI/ML use cases, grounded in trusted enterprise data
• Define integration patterns that unify data services, APIs, and AI capabilities to enable intelligent applications and automation
• Enable data accessibility, reuse, and contextualization to support cross-platform consumption, including emerging agent-driven workflows
• Establish foundational patterns for data retrieval, context enrichment, and grounding to support advanced use cases such as AI and real-time decisioning
• Architect and design enterprise-grade data pipelines supporting batch, streaming, and real-time workloads
• Build and standardize data ingestion, transformation, and distribution frameworks that power scalable data services capabilities
• Ensure delivery of high-quality, governed, and trusted data pipelines that support analytics, APIs, and downstream intelligent applications
• Implement best practices for event-driven data movement and microservices-based integration patterns
• Design data models and structures that enable data productization and consumption across analytics, APIs, and AI-driven use cases
• Architect high-performance storage solutions (including Bigtable, Spanner and other GCP services) to meet throughput, latency, and scalability requirements
• Enable secure, governed, and efficient data access patterns across operational, analytical, and real-time environments
• Serve as a trusted advisor and technical leader, mentoring teams, driving best practices, and partnering with stakeholders to identify high-value data and AI-enabled use cases

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