About Artefact
Artefact is a next-generation data and AI consulting firm dedicated to accelerating the adoption of data and AI to create measurable business impact across the full enterprise value chain.
We sit at the intersection of consulting, data science, AI technologies, data engineering, and digital transformation. We do not just advise — we build, implement, and deliver results our clients can measure. Our teams bring together consultants, data scientists, data engineers, AI engineers, analysts, and digital experts to solve complex business challenges with pragmatic, production-ready solutions.
As Artefact continues to grow globally, we are building a team of entrepreneurial data and AI talent who can help clients move beyond experimentation and into scalable, governed, value-generating AI adoption.
The Role:
Artefact is looking for a Senior Deployed AI Engineer specialized in the OpenAI ecosystem: an engineer who works embedded with our clients and takes AI products from idea to production.
You will design and build the interfaces, services, and agentic systems at the heart of our client work, such as conversational applications over enterprise data, multi-step agents that automate business workflows, and the retrieval and data pipelines that support them. You will own your components end to end: the front end, the service behind it, the data and retrieval pipelines feeding it, the deployment, and the evaluations proving it works.
This role combines deep, certified expertise in the OpenAI ecosystem (GPT and reasoning models, the OpenAI platform, and its agentic tooling) with the ability to deliver end to end. Beyond your platform specialization, you will be expected to work confidently across the full delivery lifecycle — full-stack development, data engineering, cloud infrastructure, evaluation, and client communication.
You will work closely with our clients, with direct exposure from the start, and you will support the professional development of the engineers around you.
What You'll Do
Build Full-Stack AI Applications, End to End
You will build AI products across the entire stack, from interface to infrastructure.
- Develop user-facing interfaces in TypeScript/React and the backend services and APIs behind them in Python or Node.
- Implement agentic behavior: orchestration, tool and function calling, memory, and guardrails.
- Build retrieval-augmented generation (RAG) pipelines: ingestion, chunking, embeddings, vector and hybrid search.
- Connect AI systems to enterprise data and applications via APIs, semantic layers, and protocols such as MCP.
Go Deep on the OpenAI Platform
You will be the team's reference for the OpenAI platform.
- Design and build agentic systems on the OpenAI platform: Responses API, Conversations API, and the Agents SDK — including its sandboxed execution environments and harness for long-running, multi-step, multi-tool tasks.
- Build, deploy, and optimize enterprise agents and workflow automations with AgentKit and ChatGPT Enterprise (custom GPTs, connectors, admin and governance).
- Apply the platform's core building blocks well: function calling, structured outputs, and model selection across GPT and reasoning model families for each cost, latency, and quality trade-off.
- Deliver on Azure OpenAI where clients require it, handling enterprise security, networking, and quota management.
- Use OpenAI's evaluation and fine-tuning tooling to measure and improve quality in production.
- Track OpenAI's releases closely and translate new capabilities into client value quickly.
Make AI Systems Production-Grade
Our standard is production quality: systems that are reliable, monitored, and maintainable.
- Write evaluation suites and regression tests for LLM-powered features, and monitor cost, latency, and quality in production.
- Apply solid engineering practice: version control, code review, automated testing, CI/CD, and observability.
- Deploy on cloud infrastructure (GCP, Azure, or AWS) using containers, serverless, and infrastructure-as-code.
- Build and maintain the data pipelines that feed AI systems, across warehouses, lakehouses, and vector stores.
Work AI-Natively and Client-Facing
Our engineers work AI-natively and represent Artefact directly with clients.
- Use agentic coding tools (Claude Code, Gemini CLI, Codex, Cursor) daily, with good judgment about verification and review.
- Communicate progress, trade-offs, and blockers clearly to clients and project leads.
- Support pre-sales when needed: scope solutions, build demos, and estimate effort with our partnership and consulting teams.
- Mentor junior engineers and contribute to internal accelerators, reusable components, and engineering standards.
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).
- 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 (Claude Agent 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.
- Professional English proficiency (C1/C2 minimum) — mandatory. You will work daily with international clients and colleagues.
- 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 We Offer:
- Meal (VR)
- Free Office (work from home or anywhere you want!)
- Gympass
- Insurance: Life, Health, and Dental
- Bi-monthly Meetings (our “Get Together” where we meet to be together, with workshops, lectures, training, and a happy hour!)
- Woba (you can book coworking spaces anywhere you want!)
- Semi-annual evaluations (with opportunities for promotion)
Why you should join us
- Artefact is the place to be: come and build the future of marketing
- Progress: every day offers new challenges and new opportunities to learn
- Culture: join the best team you could ever imagine
- Entrepreneurship: you will be joining a team of driven entrepreneurs. We won’t give up until we make a huge dent in this industry!
Come join us!