AI Engineer

Lasting Change Inc•Fort Wayne, IN

39d

About The Position

The AI Engineer is Lasting Change's first dedicated AI role, joining an established Data & Innovation team focused on advancing the organization's data and analytics capabilities. This position will design, build, and deploy AI-powered solutions that improve staff effectiveness and enhance services for clients. Initial focus areas include surfacing insights from complex documentation, reducing administrative burden, and supporting faster, more informed decision-decision-making across programs and operations. Working closely with organizational stakeholders and the broader data team, the AI Engineer will leverage curated data assets from Petra, Lasting Change's Microsoft Fabric-based enterprise data lakehouse, to deliver practical, trustworthy, and mission-aligned AI solutions. As the organization's AI capabilities mature, this role will help establish the standards, platforms, and practices that support long-term success.

Requirements

5–8 years of professional experience in AI, data engineering, software engineering, or a closely related field.
Demonstrated expertise in LLM integration, prompt engineering, and building AI-powered applications using modern foundation models.
Hands-on experience designing and deploying agentic AI workflows, including tool use and multi-step reasoning; familiarity with agent orchestration frameworks a plus.
Experience building or integrating MCP (Model Context Protocol) servers or equivalent agent-to-tool integration patterns.
Strong proficiency in Python and SQL; comfortable across prototyping, pipeline development, and production deployment.
Experience with cloud AI platforms; Microsoft Fabric, Azure AI Foundry, or equivalent best-in-class tooling strongly preferred.
Experience consuming organizational data platforms (lakehouses, warehouses, or similar) as inputs to AI systems.
Strong communication skills with the ability to explain AI concepts, system behavior, and trade-offs clearly to non-technical stakeholders.
Demonstrated commitment to responsible AI practices including explainability, fairness, and appropriate human oversight.
Highly organized, self-directed, and motivated by mission-driven work.

Nice To Haves

Experience with retrieval-augmented generation (RAG) architectures and vector search platforms (e.g. Azure AI Search, Pinecone, Weaviate).
Familiarity with MLOps concepts and an interest in growing into machine learning model development and deployment over time.
Exposure to healthcare, human services, or nonprofit data environments.
Microsoft Azure AI, Fabric, or equivalent cloud certifications.
Experience surfacing AI outputs through Power BI or other BI and reporting platforms.
Comfortable working in a greenfield environment where processes and patterns are still being established.
Commitment to continuous learning and professional growth in a rapidly evolving field.

Responsibilities

Design and build LLM-powered applications that help staff work more effectively — including document processing, content generation, and conversational interfaces.
Engineer prompt pipelines with structured outputs, retrieval-augmented generation (RAG), and tool-use patterns tailored to organizational data and workflows.
Evaluate, select, and integrate best-in-class LLM and AI platform tooling, with preference for Microsoft Fabric, Azure AI Foundry, and complementary services.
Ensure AI applications are reliable, auditable, and designed with responsible AI principles, including transparency, fairness, and appropriate human oversight.
Design and deploy agentic workflows that automate multi-step processes, reducing manual effort and improving consistency across operations.
Build and integrate MCP (Model Context Protocol) servers to connect AI agents with organizational data sources, internal tools, and external services.
Collaborate with operational stakeholders to identify, scope, and deliver automation opportunities with clear business value.
Contribute to a disciplined, iterative approach to AI development — shipping focused solutions, learning from them, and expanding scope over time.
Partner closely with the internal data team to leverage curated, trusted datasets from Petra as inputs to AI systems and pipelines.
Collaborate on data modeling and governance decisions that support AI use cases without compromising platform integrity.
Ensure all AI pipelines are integrated with the organization's data platform, security standards, and access controls.
Use Python and SQL fluently across prototyping, feature engineering, and production pipeline development.
Engage directly with program leaders, operations staff, and leadership to understand business problems and define AI solutions with clear, measurable outcomes.
Communicate AI system behavior, limitations, and results in plain language to non-technical audiences.
Champion responsible, explainable AI use across the organization — ensuring solutions are trustworthy and aligned with Lasting Change's mission and values.
Maintain thorough documentation of all AI systems, prompt designs, agentic workflows, and integration patterns to support maintainability and knowledge transfer.
Contribute to AI engineering standards and tooling choices that can scale as the organization's capability grows.
Leverage AI-assisted development practices to maximize engineering velocity across prototyping, documentation, and testing.
Stay current with developments in AI research and tooling; evaluate and introduce new capabilities where they create genuine organizational value.
Participate in shaping the long-term AI roadmap, including identifying when and how machine learning capabilities should be introduced over time.