Internal Forward Deployed Engineer, Entry Level

Associate Applied AI Engineer

Location: United States (Boston, MA or Cary, NC)

About Us:

We are dealer focused and OEM aligned. We are a leading ERP & DMS software and solutions provider to dealers and rental companies of heavy equipment. We specialize in working with the construction, mining, forestry, material handling, and agriculture industries. Today, we have over 800 employees, offices on four continents, and customers in over 20 countries. We are privately held, and our headquarters are in beautiful Cary, NC. We are actively seeking talented individuals to join our team and help us aggressively grow our international & North American footprint for both our 100% cloud-based ERP, DMS, and Rental solutions.

Why work for VitalEdge?

We don’t just sell technology, we enable transformation that results in broader societal benefits like building homes and critical infrastructure, growing food and delivering all sorts of products we all rely on for daily life. We exist to ultimately equip the world to keep running. We have more than 60 years of combined experience and three industry-leading software suites and associated apps, with which we will drive the market forward. It’s an exciting time to work for VitalEdge, join us!

Position Overview:

VitalEdge is building an Applied AI team to serve as the company’s AI strike team. The team will use a forward-deployed operating model internally, partnering with functional leaders and subject-matter experts to identify high-value opportunities and turn them into production-ready AI solutions.

The Associate Applied AI Engineer is an early-career builder who combines strong computer science fundamentals with curiosity about how the business operates. Working alongside the Senior Applied AI Engineer, this role will help translate business workflows into practical AI solutions, contribute production-quality code, and support deployments from prototype through adoption.

This role is designed for a recent graduate in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, or a related discipline. Success requires the ability to learn quickly, work through ambiguity, collaborate with technical and business stakeholders, and connect engineering decisions to measurable operational outcomes.

Responsibilities:

Translate Business Workflows into Technical Builds

• Work with the SeniorApplied AI Engineer, functional leaders, and subject-matter experts to understand business processes, pain points, data, constraints, and user needs.
• Turn defined business problems into technical tasks, prototypes, and production components.
• Test solutions with users, incorporate feedback, and help ensure the final product fits the operating workflow and delivers measurable value.
• Communicate progress, risks, and technical trade-offs clearly to both technical and nontechnical stakeholders.

Build and Deploy Agentic Systems

• Develop components of AI agents, multi-agent workflows, automation solutions, and AI-enabled applications under the guidance of the SeniorApplied AI Engineer.
• Build retrieval-augmented generation (RAG) pipelines, MCP-based tool integrations, APIs, and connections to enterprise applications and data sources.
• Contribute across context engineering, harness engineering, loop engineering, and graph engineering to improve agent performance, reliability, and workflow coordination.
• Create and maintain evals, tests, monitoring, feedback loops, and technical documentation forproductionAI systems.
• Write clean, tested, maintainable code and support deployment, troubleshooting, and production operations.

Build Repeatable Capability

• Contribute reusable code, prompt patterns, eval assets, technical documentation, and implementation playbooks.
• Capture lessons from each deployment so subsequent initiatives can move faster and avoid repeated work.
• Stay current on emerging AI capabilities and engineering patterns, applying them pragmatically rather than becoming tied to a single model or framework.
• Develop the technical judgment and business understanding required to independently own larger solution components over time.

Qualifications:

Required

• Bachelor’s or Master’s degree completed recently in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, ora related technical discipline.
• Strong programming fundamentals and practical Python experience demonstrated through internships, research, coursework, open-source contributions, a capstone, or personal projects.
• Understanding ofalgorithms, data structures, APIs, databases, testing, version control, and software development practices.
• Foundational experience building AI-enabled applications using several of the following: agents, orchestration, RAG, MCP or tool integrations, context engineering, harness engineering, loop engineering, graph engineering, and evals.
• Demonstrated ability to learn an unfamiliar business or technical domain, askstrong questions, and turn ambiguity into structured work.
• Clear communication skills and the ability to collaborate with engineers, functional teams, and subject-matter experts.
• Evidence of ownership, initiative, and follow-through in an academic, internship, research, or project setting.

Preferred

• Internship, research, hackathon, academic, or project experience building AI, machine learning, data, automation, or software applications.
• Experience with Git, SQL, REST APIs, cloud platforms, containers, or CI/CD workflows.
• Familiarity with frameworks such as LangGraph, LangChain, Semantic Kernel, AutoGen, CrewAI, or comparable technologies.
• Exposure to enterprise workflows, SaaS products, ERP systems, data migration, customer support, or software development operations.
• A portfolio, GitHub repository, capstone, or other work product demonstrating hands-on technical ability and problem-solving.

What Success Looks Like:

• Contributes production-quality code to priority AI deployments and connects the work to the intended business outcome.
• Learnsbusiness workflows quickly andtranslatesrequirements into reliable technical components.
• Takes increasing ownership of solution components from design through testing, deployment, and user feedback.
• Creates reusable code, evals, and documentation thatimprovethe speed and quality of future builds.
• Develops into a trusted, increasingly independent contributor within the Applied AI team.