AI Engineer

• Define and drive the vision, strategy, and roadmap for enterprise semantic data and data connectivity across business domains
• Partner with business and IT leaders to identify high-value opportunities to connect and standardize data across systems
• Position the semantic data product as a product, not just a technical asset, with clear customers, outcomes, adoption goals, and success metrics
• Define and evolve the product’s value proposition, ensuring alignment to business priorities and measurable outcomes
• Act as the general manager of the product, balancing user needs, technical feasibility, strategic priorities, operational constraints, and business impact
• Define and manage data products aligned to key business domains, including clear ownership, users, and success metrics
• Define target users and use cases for data products, ensuring alignment to specific decisions, workflows, and business outcomes
• Partner closely with business customers to ensure data products support critical decisions, workflows, and operational outcomes
• Drive adoption and usage of shared data assets across business units
• Identify and prioritize use cases where connected data unlocks measurable business value (e.g., improved decisioning, automation, personalization)
• Ensure solutions are aligned to real-world workflows and deliver tangible outcomes
• Lead the development and evolution of enterprise ontologies, taxonomies, and data models that represent key business concepts and relationships
• Establish a scalable semantic layer that enables reuse across analytics, AI, and operational use cases
• Ensure alignment of definitions across domains to reduce fragmentation and duplication
• Support both structured and unstructured data integration, enabling downstream AI and analytics applications
• Ensure data is discoverable, understandable, and usable across teams through consistent definitions and relationships
• Partner deeply with business domains to understand workflows, decision points, and data dependencies, acting as a product owner for how data supports those functions
• Own the end-to-end product lifecycle from discovery and definition to delivery and iteration
• Define product requirements, success metrics, and release plans
• Continuously refine the semantic data product based on user feedback and evolving business needs
• Define critical metrics to measure adoption, usability, and impact of the semantic data product
• Supervise usage and continuously improve accessibility and value delivery
• Evangelize the value of connected semantic data across the organization
• Partner with data governance teams to establish standards, definitions, and data quality expectations
• Promote consistency, reusability, and scalability of data assets across the enterprise

Senior Manager, AI Lead – Semantic Layer

Machine Learning Engineer

• Design and implement data-driven systems that enhance decision-making across dealer workflows
• Develop predictive or optimization logic using structured dealership data
• Translate business objectives into scalable intelligent services
• Improve consistency, accuracy, and efficiency in operational processes
• Ensure solutions are explainable, reliable, and aligned with enterprise and regulatory expectations
• Build systems that extract insights from structured dealership data (CRM, DMS, transactional records)
• Implement knowledge retrieval or search capabilities across policies and documentation where appropriate
• Collaborate with product teams to surface actionable intelligence within dashboards and workflows
• Design and deploy containerized AI services within AWS (ECS/Fargate)
• Integrate intelligent services into existing APIs and application workflows
• Monitor system performance, reliability, and integrity
• Ensure data security, auditability, and compliance alignment

AI/ML Engineer – Automotive CRM

• Define architecture patterns and technical standards for AI-enabled applications, copilots, intelligent workflows, automation agents, enterprise knowledge solutions, and reusable AI components.
• Translate business and technology use cases into scalable solution architectures, including application design, data flows, integration patterns, model usage, security controls, and operational requirements.
• Partner with the Sr. Director, AI Platform and Engineering to shape platform architecture, technical roadmaps, reference implementations, and engineering playbooks.
• Provide hands-on architecture leadership in design reviews, technical decision-making, proof-of-concept evaluation, implementation planning, and production readiness.
• Stay current on emerging AI engineering patterns, GenAI platforms, agent frameworks, model orchestration, cloud AI services, enterprise knowledge systems, and secure deployment practices.
• Design reusable platform patterns for model access, retrieval-augmented generation, vector databases, semantic search, embeddings, enterprise knowledge integration, prompt and response handling, and AI observability.
• Create architecture blueprints, technical standards, reusable components, templates, and implementation guidance that improve speed, consistency, quality, and reuse.

Principal AI Engineering Architect

• Define and lead the technical roadmap for TMG’s enterprise AI platform and AI engineering capabilities, aligned to business priorities, enterprise architecture, security standards, and governance expectations.
• Translate AI-First IT strategy into practical platform capabilities, reference architectures, engineering standards, reusable components, and delivery patterns.
• Evaluate AI platforms, cloud services, frameworks, vendor solutions, integration patterns, and development tools with a focus on security, reuse, interoperability, scalability, maintainability, and business value.
• Provide hands-on technical leadership in architecture reviews, solution design, technical decision-making, delivery planning, and complex problem-solving.
• Stay current on emerging AI engineering patterns, GenAI platforms, agent frameworks, model orchestration, enterprise knowledge systems, and responsible deployment practices.
• Partner with business and technology teams to design and deliver AI-enabled capabilities for underwriting, claims, operations, finance, customer service, and other enterprise functions.
• Establish repeatable technical patterns for moving AI use cases from proof of concept to secure, production-ready adoption.
• Build reusable accelerators and implementation playbooks that allow similar AI capabilities to be deployed across multiple business processes with less rework.
• Define architecture patterns for AI-enabled applications, copilots, intelligent workflows, automation agents, enterprise knowledge solutions, and reusable AI components.
• Establish technical standards for model access, prompt and response handling, context management, retrieval-augmented generation, semantic search, vector databases, observability, cost management, and production support.
• Lead delivery of foundational AI platform capabilities such as model gateways, model catalogs, orchestration layers, RAG frameworks, vector stores, embedding pipelines, evaluation frameworks, usage monitoring, and governance controls.
• Establish patterns for integrating AI capabilities with enterprise systems, APIs, data platforms, document repositories, workflow tools, service management platforms, and business applications.
• Create reusable engineering assets, templates, reference implementations, and deployment playbooks that improve delivery speed, quality, consistency, and reuse.
• Guide implementation of Generative AI solutions using LLMs, SLMs, embeddings, prompt engineering, RAG, semantic search, summarization, classification, extraction, and enterprise knowledge retrieval.
• Collaborate with Infrastructure and IT Operations teams to apply AI to observability, incident response, root cause analysis, predictive monitoring, runbook automation, service management, and operational productivity.

Senior Director, AI Platform and Engineering

• Design and implement scalable APIs for agent orchestration and learner interaction
• Build data pipelines that feed agent evaluation and continuous improvement cycles
• Create event streaming infrastructure for real-time learner interaction analysis
• Ensure data quality and accessibility for training and evaluating learning agents
• Improve data infrastructure for cost and performance in close coordination with Data and ML teams
• Design and implement rigorous evaluation frameworks to measure agent performance and improve cycles
• Develop LLM evaluation processes and perform error analysis to identify systemic improvements for agentic learning systems
• Support instrumentation that makes evaluation relevant (metrics, traces/logs, and analysis workflows)
• Participate in on-call rotation and incident response for learning platform reliability
• Contribute to runbooks, postmortems, and reliability improvements as we evolve our operating model

Senior Software AI Engineer

• Develop actionable insights and recommendations in support of business objectives using in-house and external data sources
• Collaborate with business and data engineering professionals for timely and reliable delivery of data solutions
• Leverage deep expertise in data engineering, data science, advanced analytics, and insurance industry data to aid business partners in risk management
• Drive efficiencies and improve customer experience
• Partner with business stakeholders to scope, structure, and drive analytical projects from requirements through delivery
• Present findings and recommendations clearly to both technical and non-technical audiences
• Source, validate, and integrate structured and unstructured data across platforms
• Conduct exploratory data analysis to reveal data quality issues and identify opportunities
• Analyze business trends, time series patterns, model performance, and causal relationships using advanced statistical and machine learning / deep learning methods
• Design, build, and deploy machine learning / deep learning models — including NLP, computer vision, and LLM-based solutions
• Create visualizations, dashboards, and reports that communicate analytical and model results to support decision-making across the organization
• Own and maintain recurring analytical, reporting, and model-monitoring processes
• Document data pipelines, model architecture, analytical frameworks, and deliverables for knowledge sharing across technical and non-technical teams

Machine Learning Scientist

• Analyze detection data including customer reports to determine which aspects of the machine learning models can be improved
• Perform tasks to enable better-management of false positive detections
• Analyze binary files to determine their legitimacy
• Review current product detections to ensure they are performing to the company standard
• Address internal questions and concerns regarding customer threat detections

Machine Learning Detection Engineer

• Develop comprehensive systems and frameworks for AI applications and products.
• Construct prototypes and minimum viable products to validate AI/ML solutions before committing substantial resources.
• Develop, refine, and productionize AI systems using cloud resources such as Azure OpenAI, Azure ML, and Cognitive Services.
• Contribute to the creation of robust data pipelines for model evolution and production systems.
• Develop specifications for low latency APIs and services necessary to deploy AI models and incorporate them into applications.
• Supervise and sustain AI systems in production to guarantee consistent model accuracy and reliability.
• Actively participate in a team that exercises principled, agile-like development practices.
• Create and maintain thorough documentation that is consistent with team procedures, corporate policies, and expectations.
• Ensure peer review on all assigned work, as well as conduct peer reviews over the work of others as requested.
• Guide junior AI engineers on AI/ML and industry best practices and methodologies.
• Keep abreast of new tools and concepts through reading documentation or literature and actively practicing skills development.
• Support and participate in meetings with external stakeholders.

AI Engineer – III

• Design, develop, and deploy scalable AI solutions
• Build LLM-based solutions for enterprise applications
• Integrate LLMs with business systems
• Architect AI agents for intelligent automation
• Implement CI/CD pipelines for machine learning models
• Monitor model performance and governance
• Collaborate with cross-functional teams for solution delivery
• Create technical documentation and mentoring

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