Senior AI Engineer

🕒 vor 15 Tagen

🌐 Kanada, Argentinien – Remote

🇨🇦 Kanada – Remote 🇦🇷 Argentinien – Remote

⏰ Vollzeit

🟠 Senior

🤖 KI-Ingenieur

👻 Geisterscore 10%

Was es bedeutet: die Wahrscheinlichkeit, dass für diese Stelle niemand wirklich einstellt.So liest du es: 60 % oder höher heißt, wir halten das für einen Geisterjob. Niedrigere Werte sind weniger bedenklich.Wie wir werten: Dauer offen, Neuveröffentlichungen, offene Stellen vs Teamgröße, wie schnell Stellen geschlossen werden, und ob ein Gehalt angegeben ist.Hinweis: eine Schätzung aus öffentlichen Daten, kein Urteil über das Unternehmen. Echte Stellen können hoch punkten, nutze es also als Hinweis, nicht als Fakt.

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Newfold Digital

WebsiteLinkedInAlle Stellen

1001 - 5000 Mitarbeiter

Gegründet 2021

💼 Beratung

📣 Marketing

📦 Logistik

💰 Venture Round im 2021-01

Consulting • Marketing • Logistics

Newfold Digital ist ein führender Anbieter von Lösungen für die Online-Präsenz, der Millionen von kleinen und mittelständischen Unternehmen weltweit betreut. Durch sein Markenportfolio, einschließlich Bluehost, CrazyDomains, HostGator, Network Solutions, Register. com, Web. com und vielen anderen, unterstützt Newfold Digital Kunden jeder Größe dabei, eine digitale Präsenz aufzubauen, die Ergebnisse liefert und Mehrwert für Unternehmen schafft. Mit einem umfangreichen Produktangebot wie Domains, Website-Baukästen, Hosting, Sicherheit, Online-Marketing, professionellem Webdesign und SEO-Services sowie einer individuellen Unterstützung arbeitet Newfold Digital mit seinen Kunden zusammen, um ihre Bedürfnisse in Bezug auf Online-Präsenz zu erfüllen.

Beschreibung

• Build production AI experiences across Network Solutions, including conversational agents, business and website creation, knowledge and FAQ agents, content experiences, domain discovery, and intelligent automation • Translate product requirements into practical AI solutions using LLMs, RAG, tool calling, agents, deterministic workflows, or traditional software where appropriate • Build production AI services using Python, FastAPI, asynchronous workers, PostgreSQL, Redis, queues, model APIs, and external tools • Implement reliable tool-calling agents and multi-step workflows interacting with internal APIs, MCP tools, business systems, and knowledge sources • Build event-driven workflows using RabbitMQ, Kafka, Azure Service Bus, or equivalent platforms, including retries, dead-letter handling, idempotency, and failure recovery • Build and improve RAG systems covering ingestion, chunking, embeddings, hybrid retrieval, reranking, metadata filtering, context construction, and citations • Integrate models from OpenAI, Anthropic, Google, xAI, and open-weight ecosystems; select models based on quality, latency, reliability, and cost • Build and run AI evaluations using curated datasets, regression tests, retrieval metrics, LLM-as-judge techniques, groundedness checks, and tool-execution evaluation • Debug hallucinations, retrieval failures, incorrect tool usage, agent loops, latency issues, provider failures, and unexpected inference costs • Use Cursor, Claude Code, OpenAI Codex, or equivalent coding agents for implementation, testing, debugging, refactoring, and documentation • Turn product goals into testable technical tasks and prototype alternatives • Measure quality, latency, reliability, safety, and cost and use those signals to improve solutions • Own implementation across APIs, workflows, data, queues, model integration, evaluations, observability, and production support for assigned features • Work with senior engineers, architects, and product partners to create reliable, secure, observable, and cost-efficient AI systems

🎯 Anforderungen

• 5 or more years of professional software engineering experience building production backend, distributed, or cloud-based systems • At least 1 year of hands-on experience building Applied AI, LLM, RAG, NLP, or agent-based applications; production experience strongly preferred • Strong Python skills, including FastAPI, asynchronous programming, Pydantic, SQLAlchemy or SQLModel, and production API development • Good backend and distributed systems fundamentals, including REST APIs, concurrency, background processing, caching, reliability, and production debugging • Hands-on experience with RabbitMQ, Kafka, Azure Service Bus, or equivalent queue and messaging architectures is required • Hands-on experience integrating LLM APIs and building structured output, function calling, tool calling, or agent execution workflows • Experience building or contributing to a RAG or knowledge-grounded system, including retrieval, embeddings, indexing, context construction, and evaluation • Working knowledge of PostgreSQL and data modeling, with exposure to Redis, pgvector, vector databases, or hybrid search technologies • Demonstrated use of AI coding agents such as Cursor, Claude Code, Codex, or equivalent tools as a regular part of software engineering work • Understanding of prompting, structured outputs, tool schemas, context management, retries, fallbacks, rate limits, caching, and streaming responses • Ability to use experiments and evaluations to compare approaches using measurable outcomes • Understanding of retrieval quality, grounding, hallucination mitigation, prompt injection risks, tool authorization, and practical agent guardrails • Experience with Docker, CI/CD, automated testing, logging, observability, distributed tracing, OpenTelemetry, Langfuse, or similar tooling • Experience with Semantic Kernel, Microsoft Agent Framework, LangGraph, PydanticAI, or equivalent agent frameworks • Experience building or consuming Model Context Protocol tools and servers, or working with model gateways such as LiteLLM • Experience with Azure AI Search, pgvector, Chroma, Kubernetes, Azure, OCI, or similar production AI platform technologies • Experience contributing to customer-facing AI products, open weight model inference, AI safety controls, automated evaluations, or high-scale backend platforms

🏖️ Vorteile

• No benefits, perks, or compensation extras are specified in the posting

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