Tekion is looking to solve the business problem of enabling AI-driven solutions across the enterprise by designing and leading the implementation of AI-ready architectures and standards that integrate data across various platforms, enabling machine learning and generative AI solutions.
Requirements
- 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.
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.
Other
- 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.
- 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.
