Descripción de la oferta
Wizeline, a global AI-native technology solutions provider, develops cutting-edge, AI-powered digital products and platforms. We partner with clients to leverage data and AI, accelerating market entry and driving business transformation. As a global community of innovators, we foster a culture of growth, collaboration, and impact.
With the right people and the right ideas, there's no limit to what we can achieve.
Sounds awesome, right? Now, let's make sure you're a good fit for the role:
Key Responsibilities
Architect and ship end-to-end agentic and LLM-powered tools for business-facing use cases, deciding when to use a single LLM call, an iterative LLM loop, or a full multi-agent system based on real task complexity.
Design AI-agnostic, model-flexible services that allow the team to evaluate and swap the best-performing model for each task.
Build production tools that transform raw content into structured, ready-to-use output — for example, systems that reformat content to defined templates/guidelines or consolidate multiple sources into a single, fact-accurate output without inventing information.
Develop and maintain RAG pipelines and vector database integrations to support retrieval-driven features such as content linking and recommendations.
Establish and scale prompt evaluation, testing, and regression-control frameworks (e.g., via Braintrust, MCP tooling, LangFuse) so quality holds as tools expand across teams and use cases.
Take AI features from prototype/PoC through to deployed, end-user-facing production tools, working across the full stack (AI core services in Python/TypeScript, front-end integration in React/Vue/Next.js) without relying on handoffs to other teams.
Partner with stakeholders and engineering leadership to gather feedback, measure impact (e.g., time saved, approval rates), and iterate on tools in production.
Extend proven architectures to onboard new use cases as configuration rather than one-off rebuilds.
Stay current on GenAI, NLP, ML, and IR technologies, incorporating best practices and cloud infrastructure to improve system efficiency.
Must-have Skills
Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related STEM field, or equivalent work experience.
4+ years of industry experience in machine learning engineering, AI engineering, or a related software engineering role.
Strong programming skills in Python and/or TypeScript/Node.js, with the ability to build both AI core services and the interfaces that consume them.
Hands-on experience deploying LLMs in production, building automated evaluation pipelines (e.g., LLM-as-a-judge), and architecting multi-agent systems that use tool-calling and long-term memory to solve non-linear problems.
Practical experience with LangChain and its ecosystem (e.g., LangGraph, LangSmith) or comparable agent-orchestration frameworks.
Experience with RAG architectures and vector databases in production settings.
Full-stack capability (front-end frameworks such as React/Vue plus back-end services on cloud infrastructure such as AWS/GCP) sufficient to ship complete features independently.
Nice-to-have
Experience with Vertex AI or equivalent multi-model cloud AI platforms.
Familiarity with prompt-management and observability tooling such as Braintrust, LangFuse, or MCP-based systems.
AI Tooling Proficiency: comfort using AI tools to optimize day-to-day work (drafting, analysis, research, automation), with the ability to recommend effective AI use and identify workflow improvements for the team.
Familiarity with Docker and Git version control.
Experience consuming and integrating third-party APIs reliably and securely.
What we offer
Commitment to Professional Development
Flexible and Collaborative Culture
Total Rewards
Specific benefits are determined by employment type and location.
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