Senior AI & Data Platform Engineer (United States)

The Senior AI & Data Platform Engineer is responsible for building, owning, and supporting the organization's internal AI and data capabilities, including its data platform, data pipelines, system integrations, and AI-powered business workflows.

Reporting to the Director of Technology & Analytics, this position serves as the organization's senior technical owner of the data platform. The Senior AI & Data Platform Engineer will lead a structured transition of data platform work currently being developed by an external consulting partner, evaluate and implement an on-premises AI environment, build and adapt AI models, skills, and agent-based workflows, and establish the testing, monitoring, and documentation standards needed to run these systems reliably in production.

This is a highly technical role. It requires strong data and software engineering fundamentals, a deep hands-on understanding of modern AI, and genuine curiosity and appetite for learning as the field changes quickly, along with the judgment to make sound architecture decisions and deliver dependable, secure solutions.

Essential Job Functions

Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

Data Platform Ownership & Transition

· Serve as the internal technical owner of the organization's data platform, including its architecture, pipelines, data models, and supporting infrastructure.

· Lead a structured transition of data platform work from the external consulting partner to internal ownership, including knowledge transfer sessions, code and documentation review, and a defined handover plan.

· Assess the current state of the platform at transition, identifying technical debt, gaps, and risks, and develop a prioritized roadmap for stabilization and improvement.

· Establish internal standards for source control, code review, environment management, and change management across the data platform.

· Manage remaining consulting engagements during and after transition, including scope, deliverables, and acceptance criteria.

Data Engineering & Integrations

· Design, build, and maintain reliable, scalable data pipelines for ingestion, transformation, storage, and retrieval across business systems.

· Develop and maintain integrations between internal applications, third-party platforms, databases, and APIs.

· Ensure data quality, integrity, lineage, and accessibility for analytics, reporting, and AI applications.

· Design data models and structures that support business intelligence and AI use cases, integrating both structured and unstructured data.

· Implement data access controls and handling practices that protect confidential and sensitive business information.

AI-Powered Business Workflows

· Design, develop, and deploy AI-powered workflows and applications that automate processes and improve decision-making across departments.

· Integrate large language models (LLMs), retrieval-augmented generation (RAG), and agentic workflows with internal data sources and business systems, including through the Model Context Protocol (MCP) and similar tool-integration standards.

· Build and maintain reusable AI assets for commercial AI platforms, such as Claude skills, plugins, and connectors, and their equivalents in ChatGPT, Gemini, and other tools.

· Fine-tune, adapt, and evaluate AI models (including open-weight models) for specific business tasks, applying sound data preparation, training, and evaluation practices.

· Help identify, scope, and prioritize high-value AI use cases based on business impact, feasibility, and return on investment.

· Measure and communicate the effectiveness and business impact of deployed AI solutions.

On-Premises AI Environment

· Define workflow, security, and performance requirements for an on-premises AI environment in collaboration with the Director of Technology & Analytics and IT & Security.

· Evaluate on-premises hardware, model hosting (including open-weight models and inference serving), and software options against defined requirements, including total cost of ownership and comparison with cloud alternatives.

· Lead implementation of the selected on-premises AI environment, including model deployment, access controls, capacity planning, and integration with the data platform.

· Maintain, patch, and optimize the on-premises AI environment in coordination with IT & Security.

Testing, Monitoring & Production Support

· Establish automated testing practices for data pipelines, integrations, and AI workflows, including data validation and AI output evaluation.

· Implement monitoring, logging, alerting, and observability to detect failures, data quality issues, and performance degradation.

· Serve as the escalation point for production issues affecting the data platform and AI systems, including root cause analysis and corrective action.

· Define and maintain service expectations, backup and recovery procedures, and runbooks for production systems.

Documentation, Governance & Security

· Create and maintain architecture diagrams, system documentation, data dictionaries, and operational runbooks so systems can be supported by others.

· Help establish and uphold AI governance standards, including responsible use, data privacy, model evaluation, and approval processes.

· Partner with IT & Security to apply security controls, access management, and secrets management across data and AI systems.

· Continuously learn and hands-on test emerging AI and data technologies, and recommend practical, cost-effective applications for the business.

Cross-Functional Collaboration & Technical Leadership

· Work with the Director of Technology & Analytics and subject matter experts, as needed, to understand operational challenges and translate requirements into technical solutions.

· Communicate technical concepts, project status, risks, and trade-offs clearly to both technical and non-technical audiences.

· Provide technical guidance and mentoring to team members, contractors, and future hires, and promote engineering best practices.

· Collaborate with the Director of Technology & Analytics on technology strategy, budgeting, and platform planning.

Qualifications

Licenses, Certifications, and/or Registrations

· Certifications are not required. Relevant cloud, data, or AI certifications (for example, from Microsoft, AWS, Google Cloud, or Databricks) are a plus, but hands-on experience carries more weight.

Education, Experience, and/or Training

· Bachelor's degree in Computer Science, Software Engineering, Data Engineering, Information Systems, or a related field preferred; equivalent professional experience or demonstrated capability will be considered.

· Master's degree in Computer Science, Data Science, Artificial Intelligence, or a related discipline is a plus.

· Typically, seven (7) or more years of professional experience in software, data, or platform engineering, or an equivalent combination of education and demonstrated capability. Hands-on experience with modern AI may come from professional, academic, or personal projects.

· Demonstrated, hands-on experience building with modern AI, such as LLM applications, RAG, agents, or model fine-tuning, through professional, academic, or personal projects.

· Strong working experience with commercial AI platforms such as Claude, ChatGPT, and Gemini, including building custom skills, plugins, connectors, or agents.

· Experience building and supporting data pipelines and data platforms, in a professional or project setting.

· Experience owning production data platforms and pipelines end to end, including support after go-live, is preferred.

· Experience taking over, stabilizing, or transitioning systems built by external vendors or consultants strongly preferred.

· Experience deploying or hosting AI models on-premises or in self-managed environments preferred.

· Experience with cloud platforms such as Microsoft Azure, Amazon Web Services (AWS), or Google Cloud Platform (GCP) preferred.

· Excellent communication, analytical, and problem-solving skills required.

Knowledge, Skills, and Abilities

• Genuine curiosity and a strong appetite for learning, with the initiative to teach oneself new tools and techniques as the AI landscape changes.
• Advanced proficiency in Python and SQL, and in modern software development practices.
• Experience with data pipeline and orchestration tools, data warehouses, and relational and non-relational databases.
• Knowledge of large language models (LLMs), retrieval-augmented generation (RAG), vector databases, prompt engineering, and agent frameworks, plus model fine-tuning and evaluation and the Model Context Protocol (MCP).
• Experience with APIs, version control, CI/CD, automated testing, and infrastructure-as-code practices.
• Understanding of containerization, GPU-based compute, and model serving for on-premises AI workloads.
• Knowledge of data security, access control, and data privacy practices.
• Ability to evaluate technology options objectively a]]>