Architect, Data Engineering (Enterprise AI Architect) at Tekion

, , United States -

Full Time

Start Date

Immediate

Expiry Date

19 Dec, 25

Salary

238600.0

Posted On

20 Sep, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Architecture, AI Solutions, Machine Learning, Generative AI, Data Governance, Data Pipelines, Large Language Models, MLOps, Cloud Data Platforms, Relational Databases, NoSQL Databases, Data Security, Privacy Controls, Strategic Thinking, Problem Solving, Team Leadership

Industry

Software Development

Description

About Tekion: Positively disrupting an industry that has not seen any innovation in over 50 years, Tekion has challenged the paradigm with the first and fastest cloud-native automotive platform that includes the revolutionary Automotive Retail Cloud (ARC) for retailers, Automotive Enterprise Cloud (AEC) for manufacturers and other large automotive enterprises and Automotive Partner Cloud (APC) for technology and industry partners. Tekion connects the entire spectrum of the automotive retail ecosystem through one seamless platform. The transformative platform uses cutting-edge technology, big data, machine learning, and AI to seamlessly bring together OEMs, retailers/dealers and consumers. With its highly configurable integration and greater customer engagement capabilities, Tekion is enabling the best automotive retail experiences ever. Tekion employs close to 3,000 people across North America, Asia and Europe. Job Description Enterprise Data Architect designs and leads the implementation of AI driven solutions across the Enterprise. Working with executives and IT leaders, they design AI ready architectures and standards that integrate data across SaaS platforms, data lakes and analytics environments, enabling machine learning and generative AI solutions. They govern data quality, accessibility and movement while curating structured and unstructured data to fuel ML, generative AI and computer vision models, ensuring that data and AI initiatives align tightly with business priorities. Key Responsibilities Architect AI‑ready data environments – Design data ecosystems that support advanced analytics, machine learning and AI‑driven insights, ensuring both structured and unstructured data are accessible, reliable and actionable. This includes integrating documents, logs and other unstructured sources into unified analytics ecosystems. Enable AI/ML pipelines and model operations – Collaborate with data scientists and ML engineers to design feature‑engineering pipelines, model training data sets and MLOps workflows that are confirmed per security policies & standards. Support real‑time data ingestion pipelines and transition ELT workflows to agile, CI/CD‑based ELT pipelines that power AI applications. Implement AI governance and compliance – Establish data governance practices specifically for AI, including metadata tagging for training data, model lineage, bias detection and privacy controls. Ensure AI data pipelines comply with ethical and regulatory requirements such as GDPR/CCPA, and align with enterprise governance frameworks. Support AI classification and agent orchestration – Design architectures that support data classification for AI models and orchestrate data flows for multi‑agent systems, ensuring that data states and knowledge are properly managed. Evaluate emerging AI technologies – Continually assess new AI/ML and generative‑AI technologies, tools and platforms. Recommend solutions that improve data quality, analytics performance and automation, and lead proof‑of‑concept initiatives to demonstrate their value. Align AI initiatives with business priorities – Ensure that AI and machine learning projects are tied to clear business objectives and deliver measurable value. Collaborate with executive leadership to prioritize AI investments and define the Enterprise AI strategy. Hands on experience with AI agents and generative AI – Demonstrate familiarity with building and integrating AI agents (e.g., conversational bots, autonomous decision making agents) and generativeAI models. Evaluate generativeAI frameworks, design governance around prompt engineering and model outputs, and guide teams on how to safely incorporate these technologies into Enterprise home-grown products, and workflows. LargeLanguage Model (LLM) training – Hands on experience fine tuning or training LLMs (e.g., GPT, BERT, Llama) using custom or domain specific data sets. Skills and Experience Education & experience Bachelor’s or master’s degree in computer science, information systems, engineering, data science or a related field. 10–15+ years of experience in data architecture or enterprise architecture roles, with at least 5 years designing enterprise data models and leading data initiatives for AI. Proven track record designing and implementing IT/data strategies and roadmaps for complex, multi‑million‑dollar initiatives. Technical expertise Deep knowledge of relational and NoSQL database technologies and cloud data platforms Experience fine tuning or training large language models (private, open source, or API-based.) along with establishing best practices for RAG, fine-tuning, orchestration, and monitoring. Ability to build and manage data pipelines for LLM training and fine tuning. Skilled in evaluating and optimizing model performance and quality. Familiarity with techniques such as transfer learning, reinforcement learning from human feedback and prompt engineering optimization. Expertise in machine learning and deep learning frameworks (e.g., TensorFlow, PyTorch, scikitlearn). Familiarity with MLOps practices and continuous integration continuous deployment pipelines. Experience implementing data security and privacy controls and ensuring regulatory compliance. Leadership & soft skills Ability to communicate complex data concepts to technical and non‑technical stakeholders and influence senior leaders. Demonstrated ability to build relationships and work across teams, including engineering, operations, product and business units. Experience leading and mentoring data & AI teams, managing project timelines and balancing competing priorities. Strong strategic thinking and problem‑solving skills; ability to translate business strategy into data architecture. Perks and Benefits Competitive compensation and generous stock options 100% employer-paid top-of-the-line medical, dental and vision coverage Great benefits including unlimited PTO, parental leave and free snacks and beverages The opportunity to work with some of the brightest minds from Silicon Valley’s most dominant and successful companies Be part of an early stage, hyper-growth start-up with the opportunity to grow and prosper Work on the latest and coolest technologies – everything is home-grown and built ground-up A dynamic work environment with a strong sense of community and collaboration The open and transparent culture that encourages innovation, rewards performance and discourages hierarchy Exciting opportunities for career growth and development Current Tekion Employees – Please apply via Greenhouse Internal Job Board The salary range describes the minimum to maximum base salary range for this position across applicable US locations. The actual compensation offered may vary from the posted hiring range based on geographic location, work experience, education, licensure requirements and/or skill level and will be finalized at the time of offer. In addition to the compensation listed, this position may be eligible for equity compensation, and a bonus or commission whereby total compensation may exceed base salary depending on individual or company performance. Your recruiter can share more about the specific salary range during the hiring process. Base Salary Range $178,950—$238,600 USD Tekion is proud to be an Equal Employment Opportunity employer. We do not discriminate based upon race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, victim of violence or having a family member who is a victim of violence, the intersectionality of two or more protected categories, or other applicable legally protected characteristics. For more information on our privacy practices, please refer to our Applicant Privacy Notice here.

Responsibilities

The Enterprise Data Architect designs and leads the implementation of AI-driven solutions across the enterprise. They ensure that data and AI initiatives align tightly with business priorities while governing data quality and accessibility.