Senior AI & Agentic Engineer

Descripción del puesto

Required Experience 3–5 years of experience in software engineering or data engineering, with extensive hands-on use of AI tools and LLM-based development over the past year (professional projects, internal initiatives, or substantial personal builds). Professional English proficiency (C1/C2 minimum) — mandatory. You will work daily with international clients and colleagues. Strong programming skills in Python and TypeScript/JavaScript, and experience building and consuming APIs. Experience with front-end development (React or similar) and at least one backend framework. Hands-on experience with RAG, embeddings, and vector search, and with at least one agentic framework (LangGraph/LangChain, Google ADK, Claude Agent SDK, or OpenAI Agents SDK). Specialization in at least one major AI platform ecosystem — Google (Gemini, Vertex AI, Gemini Enterprise), Anthropic (Claude, Managed Agents, MCP), or OpenAI (Responses API, AgentKit) — and working experience with one cloud platform (GCP, Azure, or AWS). Fluency with agentic coding tools such as Claude Code, Gemini CLI, Codex, or Cursor. Experience building and maintaining data pipelines. Bachelor's or Master's degree in computer science, engineering, or a related field, or equivalent practical experience. Certifications A certification on at least one major AI platform or cloud is a strong differentiator at application. If you do not hold one yet, obtaining one within your first 2 months in the role is a requirement — Artefact sponsors the exam and gives you time to prepare. Examples: Claude Certified Developer – Foundations (Anthropic), Google Cloud Professional Machine Learning Engineer, Google Cloud Generative AI Leader, Microsoft Azure AI Engineer Associate, or equivalent AWS credentials. Preferred Experience Experience with MCP servers, multi-agent patterns, or LLM evaluation tooling (LangSmith, Langfuse, promptfoo). Experience with Terraform or CI/CD pipelines. Key Capabilities A strong candidate will bring: Breadth across the full stack, with depth in at least one AI platform Owns features end to end, from interface to infrastructure Cares about evaluation and reliability, not just the happy path Communicates clearly in demos, documents, and code review Client-facing mindset: understands client needs and translates business requirements into technical solutions Learns new tools and models fast, and shares what works