Job Opportunities API

About Us 1GLOBAL  is a technology-driven global mobile communications provider, delivering connectivity solutions to enterprises and consumers across 190+ countries. We operate our own global mobile core network, in-house developed eSIM technology, and hold telecom licenses as a fully regulated MVNO in 12 countries and a regulated operator in 28 additional markets.  We serve the world’s leading banks, enterprises, and digital-first businesses, connecting more than 70 million people and devices globally. 1GLOBAL is profitable, with 2025 revenues exceeding US$200 million and strong cash flows funding continuous investment in platform innovation and global expansion.  Headquartered in the Netherlands, with R&D hubs in Lisbon, Berlin, and São Paulo, our team of close to 500 experts across 15 countries is united by a single ambition: to redefine global mobile connectivity through technology, scale, and execution excellence.  About the Team We are building a new AI team within Engineering, tasked with driving AI adoption across 1GLOBAL. The team’s mission is to make AI a practical, governed, and well-supported capability for every part of the company, from the engineers writing our software to the operations teams running the business. Beyond the internal platform, the team will contribute AI-powered capabilities to our commercial product lines.  This is a foundational hire. As one of the first engineers on the team, you will work alongside a dedicated product manager and engineering team lead to set the technical direction, make early architectural decisions, and build the systems that the rest of the AI function will grow on.   About the Role Desing and build central AI platform development and governance: AWS Bedrock fronted by an LLM gateway (LiteLLM, Portkey, or similar). Token budgets, rate limits, key management, guardrails, audit logging, observability, cost controls.  Engineer the technical architecture of 1GLOBAL’s internal AI tools and MCP servers, including RAG pipelines, vector search, LLM integration, and conversational interfaces.  Build secure ingestion and indexing pipelines for internal knowledge sources (Confluence, SharePoint, document repositories, internal APIs) with appropriate access controls.  Develop backend services and APIs that expose AI capabilities to internal users and, over time, to product teams building customer-facing features.  Establish engineering standards for the AI function: testing, deployment, monitoring, and model evaluation frameworks.  Own the full development lifecycle from requirements through production monitoring, with a bias toward shipping and iterating.  Requirements About You 5+ years of software engineering experience, with meaningful recent work integrating LLMs into real systems . Deep traditional ML or model-training background is not required.  Strong generalist backend engineer. Python proficiency is expected. You’re comfortable building a full-stack internal tool end-to-end and shipping it without waiting for permission.  Hands-on experience with AI gateways or model routing layers such as LiteLLM, Portkey, or AWS Bedrock — including token limits, guardrails, observability, and cost management.  Practical experience building MCP servers, or strong fluency with similar tool-calling and agent integration patterns. MCP specifically is a plus.  Power user of AI coding agents (Claude Code, Codex, or similar). You can define skills, workflows, and standards that make other engineers more effective — not just use the tools yourself.  Working knowledge of RAG, vector search, and LLM evaluation. You understand the trade-offs and can build a solid pipeline. Depth in this area is a plus, not a prerequisite.  Comfortable with CI/CD, containerization (Docker, Kubernetes), and infrastructure-a