Senior AI & Data Platform Engineer

Job Description

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.

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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.

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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.

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Essential Job Functions

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

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Data Platform Ownership & Transition

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

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·        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.

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

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

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

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Data Engineering & Integrations

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

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

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

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

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

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AI-Powered Business Workflows

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

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·        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.

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·        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.

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

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

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

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On-Premises AI Environment

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

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·        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.

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

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

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Testing, Monitoring & Production Support

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

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

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

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

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Documentation, Governance & Security

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

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

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

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

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Cross-Functional Collaboration & Technical Leadership

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

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

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

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

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Qualifications

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Licenses, Certifications, and/or Registrations

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·        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.

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Education, Experience, and/or Training

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·        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.

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

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·        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.

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

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

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

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

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

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

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

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

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Knowledge, Skills, and Abilities

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• Genuine curiosity and a strong appetite for learning, with the initiative to teach oneself new tools and techniques as the AI landscape changes.
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• Advanced proficiency in Python and SQL, and in modern software development practices.
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• Experience with data pipeline and orchestration tools, data warehouses, and relational and non-relational databases.
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• 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).
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• Experience with APIs, version control, CI/CD, automated testing, and infrastructure-as-code practices.
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• Understanding of containerization, GPU-based compute, and model serving for on-premises AI workloads.
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• Knowledge of data security, access control, and data privacy practices.
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• Ability to evaluate technology options objectively against defined business, security, and performance requirements.
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• Ability to work independently, set priorities, and manage multiple projects simultaneously.
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Special Requirements

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Tools / Equipment

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·        Computer

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Software

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·        Proficiency in Microsoft Office, including Excel, Word, and Outlook required.

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·        Experience with Python development environments, SQL, source control tools (e.g., Git), data platform and orchestration tools, AI development frameworks, commercial AI platforms (Claude, ChatGPT, Gemini), and cloud and on-premises infrastructure tooling preferred.

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Work Schedule

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·        Remote position, with occasional on-site work as needed to support on-premises infrastructure.