Senior Deployed AI Engineer – Anthropic

LinkedIn Job Wrapping · Avenida das Nações Unidas, 12901,11° andar São Paulo

  • Senior
  • Full-time
  • Posted 2026-09-21
  • Confirmed live on 25 September 2026

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Job description

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 Anthropic (Claude) 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 Anthropic ecosystem (Claude models, the Claude 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.

• Develop, schedule, and monitor data pipelines using Apache Airflow.

• Collaborate with data analysts, scientists, and engineering teams to ensure reliable data delivery.

• Optimize ETL processes for performance, reliability, and scalability.

• Maintain and improve data architecture, ensuring data integrity and security.

• Troubleshoot and resolve pipeline failures and performance bottlenecks.

• Provide technical leadership and guidance in the implementation of technical solutions, ensuring alignment with industry standards and compliance requirements.

• Document architectural decisions, strategies, and implementation guidelines for future reference.

• Lead by example, promoting engineering excellence and a culture of continuous improvement.

• Coach and upskill team members by providing technical guidance, pair programming, design reviews, and knowledge sharing.

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 Anthropic Platform
You will be the team's reference for the Anthropic platform.

• Design and build agentic systems with the Claude API and Claude Agent SDK: tool use, extended thinking, prompt caching, structured outputs, and citations.

• Build and deploy cloud-hosted agents with Claude Managed Agents: sandboxed code execution, memory, checkpointing, webhooks, multi-agent orchestration, and end-to-end tracing.

• Build and integrate Model Context Protocol (MCP) servers and clients to connect Claude to enterprise systems and data.

• Make Claude Code a core part of delivery — skills, subagents, hooks — and help clients adopt agentic development workflows.

• Deploy Claude in enterprise environments via the Claude API, Google Cloud Vertex AI, or Amazon Bedrock, selecting the right model (Opus, Sonnet, Haiku) for each cost, latency, and quality trade-off.

• Track Anthropic'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 pip

Prepare for the interview

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