Codebridge is looking for an Applied AI Engineer to build the AI functionality of a corporate learning platform end to end. Agents planning, tool use, acting across the product are the core, surrounded by retrieval, content generation, and labs. Everything is model-agnostic across Claude, OpenAI, and Gemini.<\/p>\n
Responsibilities:
Design and ship agents the core of the role: planning, tool use, memory, and guardrails, orchestrated with LangChain\/LangGraph, exposed via MCP where it fits, running behind a provider-abstraction layer with model routing and cost control
Build product features end to end from the front end through backend services to the model call in the cloud
Set up and tune retrieval: ingestion, chunking, retrieval quality, reranking, and grounded generation with citations
Evolve the content-generation pipeline: grounded generation from ingested sources, structured outputs that survive provider differences, and accuracy checks that keep generated material true to source
Develop hands-on lab environments in a sandboxed execution platform: mock APIs, auto-verification harnesses that grade learner work, and managed multi-provider model access with per-user budgets
Take charge of evaluation infrastructure: task datasets, deterministic and model-based graders, regression suites, and cross-provider benchmarks<\/p>\n
Requirements:
4+ years in software engineering, with production systems you can walk through end to end
1+ years shipping production LLM applications agents, tool use, retrieval with hands-on work across at least two of the three major platforms (Anthropic, OpenAI, Google)
Depth in agent development: tool use, memory, multi-step orchestration, and MCP you've built and debugged MCP servers, not just consumed them
Claude Code as a daily working tool, plus working fluency with the OpenAI API and Gemini or a demonstrated ability to get there fast, since the abstractions matter more than any single SDK
Evaluation fluency: task datasets, deterministic and model-based graders, regression suites. "I tested it manually and it looked fine" is an unfinished sentence
Solid Python for LLM tooling, with experience in LangChain\/LangGraph or an equivalent orchestration framework
Comfort with sandboxed cloud execution environments, CI, and API security basics
Clear technical communication you can review someone else's work rigorously and kindly, and explain a model limitation to a non-engineer without jargon
A responsible, outcomes-focused mindset
Advanced English or higher<\/p>\n
Nice to Have:
Experience with TypeScript\/Node\/React
Provider certifications (Anthropic, OpenAI, or Google Cloud AI) or demonstrable equivalent depth
Experience with learning platforms, developer education, or technical enablement
Experience with LLM gateway\/routing layers, structured-output schemas across providers, or multi-model evaluation tooling
Public evidence of technical judgment: open-source contributions, technical writing, or talks<\/p>\n
We Offer:
Technical Ownership: You own the AI architecture and the standards behind it
Modern AI Work: Agents, retrieval and evaluation as the everyday job, not a side experiment
Collaborative Environment: A team that values partnership, creativity, and mutual respect
Flexible Work: Work remotely from the comfort of your home or join us in our modern Kyiv office
Generous Time Off: 20 paid vacation days + 15 sick leave days annually
Professional Growth: Compensation for courses, certifications, and learning resources
Cutting-Edge Tools: Access to premium AI tools (Cursor Pro, Claude Code, GitHub Copilot, etc.)<\/p>\n
Recruitment Process:
HR&Technical Interview
Client stage
Offer<\/p>\n","protected":false},"excerpt":{"rendered":"
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