Internal Forward Deployed Engineer, Entry Level
About The Position
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.
Requirements
- Bachelor’s or Master’s degree completed recently in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or a related technical discipline.
- Strong programming fundamentals and practical Python experience demonstrated through internships, research, coursework, open-source contributions, a capstone, or personal projects.
- Understanding of algorithms, 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, ask strong 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.
Nice To Haves
- 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.
Responsibilities
- Work with the Senior Applied 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.
- Develop components of AI agents, multi-agent workflows, automation solutions, and AI-enabled applications under the guidance of the Senior Applied 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 for production AI systems.
- Write clean, tested, maintainable code and support deployment, troubleshooting, and production operations.
- 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.
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Entry Level
