Senior AI Engineer

๐Ÿข Quartile ยท all Quartile jobs
๐Ÿ“ Brazil
๐Ÿ“… Posted 2026-07-22 ยท via Himalayas
๐Ÿท AI-Engineering,Machine-Learning-Engineering,LLM-Development,Agentic-AI-Engineering,Software-Engineer,Senior-AI-Engineer,Senior-AI-Engineering,Senior-Lead-AI-Engineer,Senior-AI-Software-Engineer,Senior-AI-ML-Engineer,Senior-Software-AI-Engineer,Senior-Applied-AI-Engineer,Senior-AI-Analytics-Engineer,Senior-ML-Engineer,Senior-AI-Data-Engineer
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WHO WE ARE: Quartile , the world's largest retail media optimization platform, is a trusted partner for multichannel e-commerce success. Through unmatched expertise and patented AI technology, we fuel growth for 5,300+ brands and sellers worldwide and manage an annual ad spend exceeding $2 billion. The award-winning platform covers major marketplaces and ad channels for optimal reach. The result is unprecedented granularity, smarter budgeting, and bespoke solutions for retailers. Quartile is proud to be an equal opportunity employer with employees stemming from a wide range of backgrounds and experiences. As a business, we value the enrichment that diversity brings to our organization and are committed to a culture that creates a sense of inclusion and belonging. We welcome new perspectives and affirm that all employment decisions are made without regard to race, color, ancestry, religion, national origin, age, familial or marital status, sex, sexual orientation, pregnancy, gender identity or expression, disability, genetic information, veteran status, or any other classification protected by federal, state, or local law. About Sciene At Sciene, the mission is to empower professional services firms with cutting-edge, customized AI solutions โ€” enhancing automation, analytics, and optimization across industries while prioritizing security, cost efficiency, and state-of-the-art technology. Our flagship product, the Sciene AI Companion , is an autonomous customer success platform deployed across Quartile โ€” the world's largest retail media optimization platform, managing performance marketing for 1,000+ brands. It automates relationship-heavy enterprise workflows end to end: generating personalized email replies in the CSM's own voice (8x faster), building full presentation decks for client meetings (12x faster), and detecting and diagnosing account fluctuations before anyone has to ask (6x faster). None of this replaces human judgment โ€” it removes the work that was getting in the way of it. Read more about how we built it: Sciene AI Companion: Building an Autonomous Customer Success Platform on Databricks OVERVIEW: We are past the "call an LLM and hope" stage. Sciene runs a production agentic AI platform : a config-driven engine where every product is an agent with its own identity, skills, tools, and quality gates, executing ReAct loops against real business data. The Senior AI Engineer will design, build, and operate these agentic systems โ€” from prompt and context engineering, through tool and integration design (including MCP), to the evaluation harnesses and deterministic guardrails that keep LLM output trustworthy at scale. You will own features across the full model lifecycle: shipping new AI products, benchmarking models against each other with LLM-as-judge evaluation, hardening outputs with validators and enforcers, and monitoring quality and cost in production over time. REQUIREMENTS: - Strong software engineering fundamentals in Python , including modern async Python โ€” this role builds production services, not notebooks - Hands-on experience building LLM-powered applications in production : agents / tool use / function calling, prompt engineering, RAG, and structured outputs - Experience with at least one major LLM provider API (OpenAI, Anthropic, Google) and an understanding of the trade-offs between models and providers - Experience with FastAPI (or an equivalent modern web framework) and Pydantic - Understanding of how to evaluate AI systems : offline evals, LLM-as-judge, regression benchmarks, and quality metrics beyond "it looks right" - Familiarity with cloud platforms (we run on Azure โ€” Container Apps, Key Vault, Container Registry) and containerized deployment with Docker - Experience with databases in production (we use MongoDB and Databricks SQL warehouses) - Solid testing habits (pytest or similar) and comfort with CI/CD pipelines - Excellent problem-solving and analytical skills, and

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