AI Engineer
PermanentJob Overview
Veriforce is seeking a software engineer with hands-on experience building AI agents and/or working with the Model Context Protocol (MCP). You will join a growing team of talented front-end, back-end, QA, and DevOps engineers, to expand our platforms to integrate with LLMs, APIs, and enterprise data. Your work will help shape the way our clients and contractors get to work faster, stay compliant, and come home safely every day.
What that means day-to-day:
- Participate as an integral member of a cross functional full-stack team using agile methodologies
- Work in an Agile based SDLC that embraces the principles of transparency, cooperation, decomposing work, and rapid iteration
- Design and develop AI agents capable of reasoning, planning, and taking multi-step actions to support user journeys/workflows
- Build and extend MCP servers/clients for structured, interoperable AI integrations
- Integrate models with application APIs, databases, and 3rd party tools
- Ability to break complex features into manageable, reviewable, shippable pieces
- Ability to communicate clearly with engineers about implementation choices, design considerations, performance impacts, and testability.
- Ability to communicate with non-engineers about how technology is solving business needs. Including demonstrating features for feedback.
- Ability to methodically debug problems to resolve issues at the root
- Constructively and attentively review pull requests and have his or hers reviewed
- Produce code that adheres to coding standards of consistency, readability, testability, security, and maintainability. Ability to leverage AI coding assistance (i.e. Copilot, etc.).
- Write meaningful, efficient tests for important parts of an application
What you’ll need to be successful:
- 3 to 5+ years of development experience (C#, Python, TypeScript, Go, or similar)
- Hands-on experience building LLM-based agents or orchestrators
- Familiarity with MCP concepts: context servers, standardization tool integration, and protocol-base communication.
- Strong understanding of APIs, distributed systems, and cloud native architectures.
- Experience with prompt engineering, embeddings, and retrieval-augmented generation (RAG).
- Familiarity with containerization technologies, such as Docker, Kubernetes, Racher, etc.
- Knowledge of tools of contemporary engineering, specially: project boards (JIRA), source control (i.e. GitHub, Bitbucket), package managers, build systems, etc.
- Experience with, or the disposition to embrace, the usage of AI assisted coding practices