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About This Role
Description:
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\-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.
Company Conformance Statements / Essential Personal Characteristics
In the performance of their respective tasks and duties, all employees are expected to conform to the following:
- Perform quality work within deadlines with or without direct supervision.
2\. Interact professionally with other employees, customers, and clients.
3\. Work effectively as a team member.
4\. Work independently while understanding the necessity for communicating and coordinating work efforts with other employees and organizations.
5\. Exhibit exceptional integrity in all matters.
6\. Lead by example.
Requirements:
LLM Application Development
- 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.
Agentic Workflows \& Process Automation
- 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.
Data Collaboration \& Platform Integration
- 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.
Stakeholder Collaboration \& Communication
- 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.
Platform Ownership \& Continuous Improvement
- 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.
Essential Functions
Reasonable accommodations may be made to enable individuals with disabilities to perform these functions.
- Use of Fingers
- Feeling
- Speaking
- Hearing
- Repetitive Motions
- Capable of making sound decisions by use of reasonable and logical judgments.
- Demonstrated competence in understanding, interpreting, and communicating procedures, policies, information, ideas, and instructions.
Travel
Travel may be required occasionally to subsidiary sites and training opportunities.
Required Experience
- 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.
Preferred Qualifications
- 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.
Other Duties
This job description is not designed to cover or contain a comprehensive listing of activities, duties, or responsibilities that are required by the employee. Management reserves the right to assign or reassign duties, activities, and responsibilities to this position at any time, with or without notice.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Lasting Change Inc, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Lasting Change Inc AI Hiring
Lasting Change Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Fort Wayne, IN, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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