Senior Forward Deployed AI Engineer (Openai)

What We're Looking For 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 hands-on experience with the OpenAI ecosystem: Responses API or Agents SDK, function calling, and prompt engineering for GPT and reasoning models — ideally with experience taking at least one solution to production (OpenAI API or Azure OpenAI). • 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 (OpenAI Agents SDK, LangGraph/LangChain). • Working experience with at least one cloud platform; Azure experience is a strong plus for Azure OpenAI delivery. • 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 Certifications are a strong differentiator at application. OpenAI's proctored certification program is still rolling out publicly, so where a formal OpenAI credential is not yet available to you, we expect you to obtain the closest available credential within your first 2 months in the role — Artefact sponsors the exam and gives you time to prepare. • OpenAI Academy certifications and badges, as they become generally available. • Microsoft Certified: Azure AI Engineer Associate is highly valued for Azure OpenAI delivery. Preferred Experience • Experience with MCP servers, multi-agent patterns, or LLM evaluation tooling (LangSmith, Langfuse, promptfoo). • Experience with Terraform or CI/CD pipelines. • Experience with realtime/voice APIs, multimodal applications, or fine-tuning at scale. Key Capabilities A strong candidate will bring: • Deep expertise in the OpenAI platform, combined with breadth across the full stack • Owns features end to end, from interface to infrastructure • Cares about evaluation and reliability, not just the happy path • Communicates clearly with clients 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

What You'll Do Artefact is looking for a Senior Deployed AI Engineer specialized in the OpenAI ecosystem: embedded with clients, taking AI products from idea to production. You'll design and build the interfaces, services, and agentic systems at the heart of our client work: conversational apps, agents automating workflows, and pipelines supporting them. You own components end to end: front end, service, data, deployment, evals. Build Full-Stack AI Applications, End to End • Develop interfaces in TypeScript/React and backend services/APIs in Python or Node. • Implement agentic behavior: orchestration, tool/function calling, memory, guardrails. • Build RAG pipelines: ingestion, chunking, embeddings, vector/hybrid search. • Connect AI systems to enterprise data via APIs, semantic layers, and MCP. Go Deep on the OpenAI Platform • Build agentic systems on OpenAI: Responses API, Conversations API, Agents SDK, incl. sandboxed execution for long-running tasks. • Build and optimize enterprise agents with AgentKit and ChatGPT Enterprise (custom GPTs, connectors, governance). • Apply function calling, structured outputs, model selection across GPT/reasoning families per trade-off. • Deliver on Azure OpenAI where required: security, networking, quota. • Use OpenAI's eval and fine-tuning tooling to improve quality. • Track OpenAI's releases and translate capabilities into client value. Make AI Systems Production-Grade • Write evals and regression tests; monitor cost, latency, quality. • Apply solid practice: version control, review, testing, CI/CD, observability. • Deploy on GCP/Azure/AWS using containers, serverless, infra-as-code. • Build and maintain data pipelines feeding AI systems. Work AI-Natively and Client-Facing • Use agentic coding tools (Claude Code, Gemini CLI, Codex, Cursor) daily, with good judgment. • Communicate progress, trade-offs, and blockers to clients and leads. • Support pre-sales: scope solutions, build demos, estimate effort. • Mentor junior engineers; contribute to accelerators and standards.