Artificial Intelligence

• 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

Senior Data Architect – AI

• Collaborate with strategic clients to identify where AI and data can drive meaningful action — not just generate insights
• Translate client business challenges into technical approaches that are both rigorous and achievable
• Serve as a trusted advisor who can simplify complex AI concepts for non-technical stakeholders
• Provide hands-on technical direction for generative AI projects, including architecture decisions and solution design
• Apply expertise in LLMs, data science, and object-oriented programming to guide delivery from prototype to production
• Leverage tools like Databricks, Python, R, and SQL across the project lifecycle — including building and managing data pipelines, feature engineering, and model deployment within the Databricks ecosystem
• Lead and develop a cross-functional team of data scientists, AI engineers, and software engineers
• Set clear expectations, support career growth, and build a team culture oriented around craft and client outcomes
• Model the technical and communication standards you expect from the team
• Drive alignment across AI, analytics, strategy, and engineering disciplines within OneMagnify
• Partner with delivery and operations teams to ensure AI workstreams integrate cleanly into broader project execution
• Represent the AI practice in client-facing settings alongside strategy and creative counterparts
• Stay current on the evolving AI landscape — new models, platforms, tools, and use cases — and bring relevant developments into client conversations and internal delivery
• Contribute to OneMagnify's point of view on AI-enabled solutions and how the practice continues to grow

Manager, Artificial Intelligence

• Develop and deploy production-grade ML models and AI systems for diagnostics, predictive maintenance, and anomaly detection supporting product health.
• Build and optimize LLM-based applications, including Retrieval-Augmented Generation (RAG) systems for technical documentation and automated analysis.
• Implement predictive models for failure detection, remaining useful life, and early warnings.
• Apply advanced ML techniques such as deep learning, NLP, time-series forecasting, and generative AI.
• Create scalable data pipelines and real-time inference systems.
• Collaborate with cross-functional teams and mentor junior engineers.
• Present insights to both technical and non-technical stakeholders and drive adoption of ML solutions through dashboards and APIs.

Senior AI/ML Scientist

• Run the 30-day AI Jumpstart Discovery cycle: kickoff, eight to fifteen department-level event-storming interviews, AI-assisted analysis, executive prioritization workshop, and final readout.
• Apply our pain-first discovery method. You surface drudgery, manual handoffs, bottlenecks, and broken approvals before any tool conversation. The "tool-first" trap is the failure mode we exist to prevent.
• Facilitate executive prioritization workshops that resolve a portfolio of 15 to 30 candidates into a ranked Top 5: three quick wins (high value, low risk) and two strategic bets (high value, high complexity), each defensible with math.
• Co-design current-state and future-state BPMN (Business Process Model and Notation) maps with department owners, applying our Digital Assembly Line model to identify where AI, document understanding, and programmatic automation replace manual toil.
• Translate findings into ROI projections the CFO will accept — labor reallocation, cycle-time reduction, denial-rate movement, throughput per FTE, audit exposure, prior-auth turnaround, whatever the workflow demands.
• Direct the four-agent deliverable pipeline that converts transcripts into client-ready artifacts (opportunity extraction → current/future BPMN → department reports → org-wide consolidation). You do not operate it line by line — that is what the Product Analyst on your engagement does — but you set quality, own QA against transcripts, and sign off before anything reaches the client.
• Address the blockers that prevent AI from reaching enterprise-grade maturity in real operations: data readiness, integration complexity, change-management friction, HIPAA and PHI boundaries, governance constructs the client cannot articulate yet.
• Act as the feedback loop into PTM's Fusion methodology and tooling. Every engagement you run produces field signal that updates our interview guide, our prompt library, our BPMN style guide, and our healthcare-vertical playbook. Your work compounds across the practice.
• Carry the relationship from Discovery into the implementation Statement of Work. You participate in scope development and maintain continuity into the build phase.

Forward Deployed Engineer – AI Consultant

• Gain practical experience in implementing GenAI and Agentic AI use cases
• Contribute to ongoing projects and learn from real-world challenges
• Assist with research, data analysis, and development tasks
• Participate in team meetings, trainings, and presentations

AI Analyst

• Provide proactive and reactive security services to safeguard Ford’s technology, infrastructure, applications, and data.
• Develop autonomous agents designed to analyze massive, complex datasets to identify "weak signals" and stealthy adversary behaviors that traditional SIEM and EDR tools often miss.
• Translate complex AI concepts into actionable security outcomes while mentoring peers on emerging automated defense techniques.
• Design, develop, and deploy autonomous AI agents to automate complex threat hunting tasks, alert triage, and incident investigations.
• Execute hypothesis-driven hunting campaigns using AI/ML to identify anomalies, lateral movement, and "living-off-the-land" techniques across enterprise datasets.
• Lead coordinated responses to major intrusions, phishing, and misuse of computing facilities using EDR, SIEM, and Cloud logs to minimize asset loss and threat propagation.
• Build and maintain RAG (Retrieval-Augmented Generation) systems and agents that correlate telemetry across endpoint, network, identity, and cloud environments.
• Develop consistent and repeatable methods to resolve security incidents, ensuring high-quality results are delivered in a timely manner.
• Ensure all incident response and data handling activities enable compliance with global laws, regulations, and due diligence requirements.

Cyber Defense Response Analyst, AI & Automation Focus

• Focus daily effort selling to Small Law customers that are new subscribers within one to ten attorney size firms within an assigned territory
• Achieve and exceed monthly and annual quotas through negotiations and product offering within set parameters
• Achieve outbound prospecting and opportunity creation goals using tools such as outbound calls, emails, and LinkedIn Sales Navigator
• Gather and log all call information, sale orders, and profile activity within Salesforce, Thomson Reuters CRM system, correctly and accurately, ensuring proper marketing sources are credited appropriately
• Develop and deliver accurate sales forecasts
• Build, maintain and enhance client's experience through consultative conversation
• Work with organizations (Order Fulfillment, Credit, Customer Service, etc.) throughout Thomson Reuters to ensure customer accounts are handled efficiently and accurately
• Actively contribute to and works responsibly in a team environment, including collaborating with Field Sales Reps and Full Inside Sales Executives
• Obtain customer and competitive information during daily contacts and distribute as appropriate to develop sales strategies
• Maintain a working knowledge of company products, special sales programs and marketing efforts within the sales division
• Actively participate in meetings, training sessions and individual professional development and skill building

Acquisition Sales Specialist – AI Solutions

• The Staff AI/ML Vehicle Motion Control Engineer will be a key technical leader in GM’s Vehicle System Controls organization, on a team specifically focused on AI‑based control, machine learning, and advanced vehicle motion control.
• You will set the technical direction for how GM combines state‑of‑the‑art control theory with modern AI/ML methods to achieve step‑change improvements in handling, comfort, safety, and efficiency.
• This includes classical controllers and estimators, as well as learning‑based models and policies for vehicle motion control across braking, steering, chassis, and integrated dynamics.
• You will collaborate closely with teams in vehicle dynamics, ADAS/AD, perception, software, and safety to architect and deliver AI‑enabled motion control platforms that are robust, explainable, and production‑ready.

Staff AI/ML Vehicle Motion Control Engineer – Vehicle System Controls

• 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 aligned with growth, productivity, quality, safety, and risk objectives.
• 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, implement, and continuously evolve the enterprise AI operating model, including governance forums, funding and intake processes, prioritization methods, and decision rights.
• Chair and run the Executive AI Steering Committee, ensuring disciplined decision making, clear accountability, and execution follow through; set agendas, align key stakeholders, and communicate decisions and actions clearly.
• Establish enterprise standards for responsible AI, data usage, security, privacy, and risk management in partnership with Legal, Cybersecurity, IT Architecture, HR, and Compliance.
• Own AI investment governance, ensuring alignment with capital planning, transformation funding, and enterprise portfolio priorities.
• Own the global AI use case portfolio and prioritization framework, balancing short term value delivery with long term strategic capability building.
• Ensure AI initiatives progress efficiently from ideation to pilot to scaled production—eliminating “pilot only” stagnation.
• Define, track, and report enterprise AI value metrics, including operational efficiency, cost reduction, quality improvement, risk mitigation, revenue enablement, and customer impact.
• Regularly recalibrate the AI roadmap based on realized outcomes, business performance, and evolving priorities.
• Orchestrate cross-functional delivery across IT, data, digital platforms, business units, and external partners—creating shared plans, clear ownership, and fast decisions.
• Resolve enterprise level dependencies and escalations impacting delivery, adoption, or value realization by facilitating trade-off discussions, aligning decision makers, and driving timely, documented outcomes.
• Ensure AI initiatives are integrated with core enterprise platforms (ERP, data environment, digital products) rather than delivered as isolated solutions.
• Build scalable execution playbooks, delivery standards, and reusable components to accelerate adoption across regions and functions.
• Partner with HR and Communications to lead enterprise-wide change management, AI literacy, and workforce enablement.
• Sponsor AI capability building, including training programs, citizen developer models, communities of practice, and leadership education.
• Drive cultural adoption by positioning AI as a core business capability embedded in how work gets done—not as a technology experiment.
• Support workforce transformation planning, including role evolution, ethical considerations, and future skills development.
• Build, lead, and develop a high performing enterprise AI organization with clear roles, succession, and career paths.
• Set performance expectations, accountability, and development plans for direct and indirect reports.
• Foster a strong culture of accountability, collaboration, and execution discipline across the AI ecosystem.

VP Enterprise AI Enablement

• Architect AI Agents: Develop and deploy custom AI agents to automate the monitoring of Design Development Checklists, data pedigree, and property setup integrity.
• Vibe Coding & Visual Scripting: Use natural language-based programming (vibe coding) to create bespoke Python scripts or macros within Catia/3DX that automate repetitive "Studio property" setups and manikin drops.
• Generative 3D Scaffolding: Lead the development of Surface Scaffold files by integrating AI-assisted 3D modeling tools (e.g., Vizcom, Luma AI, or Generative CAD) to rapidly explore "what-might-be" scenarios before committing to formal A-surface.
• Intelligent Feasibility: Lead the completion of studies within Design Feasibility Checkpoint Process by using legacy and predictive AI models to identify potential hardpoint or legislative conflicts earlier than traditional methods allow.
• Augmented Integration: Lead the integration of A-surface with thematic and functional inputs, using AI to ensure the emotive character of the art is preserved while meeting strict engineering and attribute requirements.
• Provocation & Optimization: Provide concept alternatives through generative design prompts, using AI to provoke new ways of reconciling thematic intent with program assumptions.
• Data Orchestration: Manage the flow of data into and out of the studio using automated pipelines, ensuring the "data pedigree" is maintained via intelligent version control.

Studio Engineer – AI, Generative Workflows

• Develop and execute AI and innovation communications strategies across product launches, partnerships, customer stories, and executive thought leadership.
• Lead end-to-end communications for product launches, partnership announcements, and customer stories.
• Prepare executives for media interviews, speaking engagements, and analyst conversations.
• Build and maintain relationships with technology, business, and trade media covering AI, legal tech, fintech, and professional services.
• Coordinate with marketing, sales, product marketing, analyst relations, and regional teams.

See all jobs on JobTailor

AI Orchestration Engineer Intern

Quality Systems Manager – Digital Quality Platforms, AI

Chief Engineer, AI Product Creation

VP of AI, ML and Data Strategy

Director, Enterprise AI Enablement

Director, Applied AI – Strategy

AI/ML Staff Researcher

AI Strategy Associate Director

Senior Data Architect – AI

Manager, Artificial Intelligence

Senior AI/ML Scientist

Forward Deployed Engineer – AI Consultant

AI Analyst

AI Analyst

Cyber Defense Response Analyst, AI & Automation Focus

Acquisition Sales Specialist – AI Solutions

Staff AI/ML Vehicle Motion Control Engineer – Vehicle System Controls

VP Enterprise AI Enablement

Studio Engineer – AI, Generative Workflows

Director – AI and Innovations Communications