About this role
We are:
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.
Are you a fit?
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
- A High-Impact Environment
- Commitment to Professional Development
- Flexible and Collaborative Culture
- Global Opportunities
- Vibrant Community
- Total Rewards
Specific benefits are determined by employment type and location.
Find out more about our culture here .