AI Engineer, Freelance

Overview

Build production agents — design, develop, and deploy goal-oriented AI agents and multimodal/conversational experiences using frameworks like Google ADK, Langchain, LangGraph, combined with orchestration tools like n8n where appropriate. Own the production lifecycle of what you ship — establish robust AIOps/AgentOps practices (monitoring, versioning of agent blueprints, evaluation pipelines, reliability) within your team's scope. Contribute to the shared Agentic Platform (Core pillar) — gateways, evaluation frameworks, observability, MaaS/AaaS APIs — so feature teams build faster on solid foundations. Build agent-side integrations (Feature pillar) — develop the MCP servers and backend services that agents need to interact with enterprise systems, in partnership with other R&D teams (and picking up the work yourself when a partner team doesn't have bandwidth). Be the Python referent in your team — own production-quality Python, enforce strong SWE principles (unit tests, CI/CD, Git, code review), and bring AIOps/MLOps best practices wherever you sit. Build a working expertise on agentic design patterns (eval, guardrails, multi-agent orchestration) and share it with AI champions and AI builders across the company as the GenAI Center of Expertise takes shape. Engage with stakeholders — talk to internal teams to understand operational pain points and translate them into measurable GenAI solutions. You don\'t lead cross-team architecture, but you should be credible across the org.

Requirements

• Master\'s degree in Computer Science, Data Science, or a similar technical field.
• 3+ years as a Python Developer or ML Engineer, with a recent focus on deploying LLM-powered solutions in production.
• Mastery of Python for enterprise-level development, strong knowledge of core software engineering principles, and hands-on experience with AIOps/MLOps (unit tests, CI/CD, Git, observability). You should be comfortable being your team\'s reference on these topics.
• Proven experience building production-ready agent workflows, orchestration layers, or platforms using Python frameworks (ADK, Langchain, LangGraph).
• Practical experience with modern cloud platforms (Vertex AI, Kubernetes or equivalents) for deploying scalable GenAI services.
• Strong versatility and a demonstrated willingness to work across the full stack and across functional expertises.
• Entrepreneurial mindset, high autonomy, and the ability to turn ambiguous, high-level business goals into concrete, efficient GenAI features.
• Fluent technical English (written and verbal) — mandatory.