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.

This role bridges business strategy and technical execution. The Senior Applied AI Engineer will combine hands-on engineering capability with the business judgment required to determine what should be built, how it should fit into the operating workflow, and what is required to drive adoption and measurable value. This role reports directly to the CEO.

The role will remain close to the code while leading a small team of Associate Applied AI Engineers across initiatives such as data migration, ticket deflection, and agentic software development. Success requires curiosity about how the business operates, comfort working through ambiguity, and the ability to simplify complex problems into pragmatic solutions.

Responsibilities:

Translate Business Priorities into Production Solutions

• Partner with functional leaders and subject-matter experts to understand workflows, economics, constraints, user needs, and the underlying business problem.
• Identify the highest-value intervention points and translate ambiguous operational challenges into clear solution designs, delivery plans, and measurable outcomes.
• Own solutions from discovery and technical scoping through build, deployment, user adoption, and value realization.

• Balance technical sophistication with speed, usability, operating fit, and business impact.

Architect and Build Agentic Systems

• Design and develop production-grade AI agents, multi-agent workflows, and orchestration patterns.
• Build retrieval-augmented generation (RAG) systems, MCP-based tool integrations, APIs, and connections to enterprise applications and data sources.
• Apply context engineering, harness engineering, loop engineering, and graph engineering to create reliable, maintainable agentic systems.
• Establish evals, observability, security, testing, guardrails, and feedback loops for production AI systems.
• Contribute directly to code and make pragmatic trade-offs across scope, speed, quality, cost, and maintainability.

Lead the Pod and Build Repeatable Capability

• Provide technical leadership, coaching, and hands-on guidance to the Associate Applied AI Engineers.
• Set fit-for-purpose engineering standards, review solution designs and code, and remove delivery blockers.
• Codify reusable architectures, libraries, implementation playbooks, and deployment patterns.
• Create a repeatable operating model that enables VitalEdge to scale successful AI solutions across the enterprise.

Qualifications:

Required

• Bachelor’s degree in Computer Science, Software Engineering, Artificial Intelligence, or a related discipline, or equivalent practical experience.
• 6+ years of experience building and deploying production software, data, automation, or AI solutions; exceptional candidates with less tenure and significant applied AI experience will also be considered.
• Strong proficiency in Python and sound software engineering fundamentals, including version control, testing, CI/CD, APIs, and cloud-native development.
• Demonstrated experience building production AI applications using several of the following: agent development, multi-agent orchestration, RAG, MCP or tool integrations, context engineering, harness engineering, loop engineering, graph engineering, and evals.
• Proven ability to understand an operating workflow, identify the underlying business problem, and translate it into a pragmatic technical solution.

• Experience owning solutions beyond initial development, including testing, deployment, user feedback, adoption, and measurable outcomes.
• Clear communication skills and the ability to work effectively with engineers, functional leaders, and subject-matter experts.
• Experience mentoring engineers or leading the technical delivery of a small team.
• Experience with enterprise workflows such as ERP implementation, data migration, customer support, or software development.

Preferred

• Experience building AI solutions in an enterprise, SaaS, startup, consulting, solutions engineering, or internal transformation environment.
• Familiarity with LangGraph, LangChain, Semantic Kernel, AutoGen, CrewAI, or comparable technologies.
• Experience establishing evals, observability, governance, and reliability controls for production AI systems.
• Full-stack or cloud application development experience.