Agentic Engineer

<div class="content-intro"><p><strong>Build the Future with AspenView Technology Partners</strong></p> <p>At AspenView, we are passionate about transforming the way organizations approach technology. We specialize in creating high-performing, nearshore IT teams to help North American clients innovate faster and more efficiently.</p> <p>As we continue to grow, we’re looking for exceptional people to join our team and help drive impactful change across industries.</p></div><p><strong>About the Role</strong></p> <p>AspenView is hiring an Agentic Engineer to build, tune, and measure agentic delivery across payments and hospitality commerce for a global payments and financial-technology platform. The client is putting agents to work inside its software delivery and its banking and commerce workflows, and its AI estate is already live and large: an agentic operating system for banking built on OpenAI with Amazon Bedrock and AWS AgentCore, AI-assisted development across 8,000+ engineers, and an autonomous coding agent on core modernization. Restaurant, lodging, and hospitality commerce is a named growth vertical, so domain fluency shortens the ramp materially.</p> <p>You will design and build agentic workflows, engineer the context and tool surface each agent runs on, prove results with evaluation harnesses and hard numbers, and harden everything for a regulated payments environment. We hire for demonstrated hands-on work and judgment rather than one career path — we assess the work, not the job title — and regulated-environment governance is non-negotiable.</p> <p><strong>What You Will Do</strong></p> <p><strong>Agent Design & Context Engineering</strong><br>- Design and build agentic workflows — single agents, multi-agent orchestration, and subagent decomposition — against real delivery and operational tasks.<br>- Engineer the context: retrieval and RAG strategy, scoping, structured summaries, and pruning rules that keep long sessions coherent and affordable.<br>- Own system prompts, agent instructions, and configuration as versioned, reviewed artifacts rather than ad hoc text passed between people.<br>- Design the tool surface each agent gets — tool inventories, MCP servers, and permission and sandboxing models — connecting agents to repositories, Azure DevOps, CI, documentation, and data platforms.<br>- Build on the client's existing AI estate: OpenAI APIs and the Codex toolchain, agent runtimes on Amazon Bedrock / AWS AgentCore, GitHub Copilot, and Microsoft Foundry.</p> <p><strong>Evaluation & Measurement</strong><br>- Build evaluation harnesses on representative task sets — including payment, POS, and hospitality commerce flows — and instrument runs for traces, token and latency telemetry, and tool-call success.<br>- Track and report throughput, quality, human intervention rate, and cost per unit of work against a pre-agent baseline.</p> <p><strong>Security, Governance & Enablement</strong><br>- Define the human-in-the-loop gates: what an agent may complete unattended, what needs review, and what it must never touch.<br>- Harden workflows against prompt injection, untrusted tool output, secret leakage, and unsafe autonomous actions, with identity-bound execution and the audit trail a regulated payments client expects.<br>- Codify what works into reusable configuration and playbooks, and enable other engineers to use it.</p> <p><strong>What You Bring</strong></p> <p><strong>Education</strong><br>- Bachelor's degree in Computer Science, Software Engineering, or a related technical field, or equivalent practical experience.</p> <p><strong>Experience</strong><br>- Substantial hands-on delivery in a technical role — software, AI/ML, data or automation engineering, or technical leadership of engineering teams.<br>- Travel or hospitality domain experience is mandatory — restaurant, lodging, POS, booking, ticketing, or travel-payments systems, where folios, incremental authorisations, no-show and cancellation handling, tip adjustments, and multi-property settlement are familiar ground.<br>- Hands-on production work with OpenAI — APIs, the Codex toolchain, or agent frameworks built on OpenAI models.<br>- Production LLM or agentic systems that real users depended on: agents, agentic workflows, RAG, or LLM-assisted automation.<br>- Practical use of AI coding and agentic tools — Codex, Claude Code, Cursor, GitHub Copilot, or comparable — well beyond casual use.</p> <p><strong>Technical Expertise</strong><br>- Python and/or TypeScript at working level, comfortable building tooling and integrations.<br>- Working understanding of LLM mechanics: context windows, tokenisation, retrieval, tool calling, and prompting — and where each breaks.<br>- Version control and CI/CD familiarity — Azure DevOps or GitHub Actions.</p> <p><strong>Soft Skills</strong><br>- A measurement mindset — baseline first, instrument everything, and let the data settle trade-offs.<br>- English at C1 level, able to explain trade-offs to engineers and executives alike.</p> <p><strong>Nice to Have</strong><br>- Payments or financial-services delivery — acquiring, issuing, core banking, or merchant processing.<br>- Amazon Bedrock, AWS AgentCore, Microsoft Foundry, or Azure OpenAI at production scale.<br>- MCP server design, tool inventories, and agent-integration surfaces.<br>- RAG and retrieval tuning — embeddings, vector stores, hybrid search.<br>- Evaluation harnesses, golden task sets, LLM-as-a-Judge.<br>- Token and cost optim