Applied AI Engineer

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.

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

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

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

What 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.)

Recruitment process

HR&Technical Interview

Client stage

Offer