Senior Deployed AI Engineer (Openai)
Summary
Build and deploy full-stack AI applications using OpenAI's ecosystem, from conversational apps to agentic systems, end-to-end in TypeScript/React and Python/Node, with RAG pipelines and enterprise integrations.
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.
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