The Role
Build and deploy production-grade AI tools using LLMs, agentic systems, RAG pipelines, vector databases, and automated evaluation frameworks. Develop flexible AI services and full-stack integrations with Python, TypeScript, and modern frontend frameworks. Take solutions from prototype through production, measure business impact, collaborate with stakeholders, and extend reusable architectures across use cases.
Summary Generated by Built In
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- Design, develop, and enhance scalable content and translation platforms using Java and Spring Boot, REST APIs, and microservices architectures.
- Build and integrate LLM- and AI-powered translation capabilities into enterprise content-processing workflows.
- Develop reliable APIs and backend services for translation requests, processing, and results, ensuring scalability, performance, and maintainability.
- Integrate with AI/LLM and machine-translation providers, evaluating opportunities to improve translation quality, scalability, cost, and latency.
- Design and implement event-driven architectures using technologies such as Apache Kafka to support high-volume content-processing pipelines.
- Integrate translation and content services with existing enterprise platforms, APIs, and downstream systems.
- Develop solutions for multilingual content processing, supporting multiple languages, content types, and international publishing requirements.
- Implement robust error handling, retries, monitoring, logging, alerting, and other operational capabilities required for reliable production systems.
- Develop automated tests and validation mechanisms to ensure the quality and reliability of translation workflows and services.
- Contribute to performance and scalability improvements as content and translation volumes increase.
- Support production deployments, troubleshooting, incident resolution, and ongoing operational improvements.
- Collaborate closely with engineering, product, and client stakeholders to define technical solutions, participate in design discussions, and translate business requirements into scalable software.
- Contribute to technical documentation, code reviews, CI/CD practices, and engineering standards.
- Help evolve the platform architecture to support future international expansion, additional languages, and new content types.
- Stay current with developments in GenAI, LLMs, machine translation, multilingual NLP, cloud technologies, and distributed systems, applying relevant practices to production solutions.
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related STEM field, or equivalent practical experience.
- 4+ years of professional software engineering experience, with strong experience developing enterprise applications using Java and Spring Boot.
- Strong experience designing and developing REST APIs and microservices.
- Hands-on experience with cloud-based application development, preferably on Google Cloud Platform (GCP).
- Practical experience with Apache Kafka or similar event-driven messaging technologies.
- Experience developing or integrating LLM/AI-powered applications and APIs in production or enterprise environments.
- Experience with machine translation, multilingual content processing, NLP, or related AI-driven content technologies.
- Strong understanding of software engineering practices, including Git, CI/CD, automated testing, code reviews, and Agile methodologies.
- Experience integrating applications with existing enterprise platforms, APIs, and third-party services.
- Ability to troubleshoot production issues and contribute to monitoring, logging, performance optimization, and operational support.
- Strong written and verbal English communication skills, with the ability to communicate effectively with technical teams and stakeholders.
- Experience with content-management, publishing, media, or digital content platforms.
- Experience working with LLM providers, AI APIs, or machine-translation platforms such as Google Cloud Translation, Azure AI Translator, AWS Translate, or equivalent services.
- Familiarity with Google Cloud Platform services such as GKE, Cloud Run, Pub/Sub, Cloud Storage, BigQuery, or similar.
- Experience with multilingual NLP, translation quality evaluation, language detection, or localization workflows.
- Familiarity with AI/LLM orchestration frameworks and tools such as LangChain, LangGraph, LangSmith, or equivalent.
- Experience implementing observability solutions using tools such as Cloud Monitoring, Cloud Logging, Datadog, Grafana, or equivalent.
- Experience with Docker and containerized application deployments.
- Experience working with high-volume, distributed, or latency-sensitive systems.
- Familiarity with media or publishing workflows, content pipelines, editorial systems, or international content distribution.
- AI Tooling Proficiency: comfort using AI tools to optimize day-to-day work, including development, analysis, research, testing, and automation, with the ability to identify opportunities to improve engineering workflows.
- 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.
Skills Required
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, a related STEM field, or equivalent work experience
- 4+ years of industry experience in machine learning engineering, AI engineering, or related software engineering
- Strong programming skills in Python and/or TypeScript/Node.js
- Experience deploying LLMs in production
- Experience building automated evaluation pipelines, including LLM-as-a-judge systems
- Experience architecting multi-agent systems with tool-calling and long-term memory
- Practical experience with LangChain and its ecosystem, such as LangGraph and LangSmith, or comparable agent-orchestration frameworks
- Production experience with RAG architectures and vector databases
- Full-stack capability with frontend frameworks such as React or Vue and backend cloud services such as AWS or GCP
- Experience with Vertex AI or equivalent multi-model cloud AI platforms
- Familiarity with Braintrust, LangFuse, or MCP-based prompt-management and observability systems
- Experience using AI tools to optimize work and identify workflow improvements
- Familiarity with Docker and Git
- Experience integrating third-party APIs reliably and securely
Wizeline Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Wizeline and has not been reviewed or approved by Wizeline.
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Healthcare Strength — Core coverage spans medical, dental, vision, life and disability insurance, with private medical commonly included in Mexico. Additional support such as EAP and wellness programs is also referenced.
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Leave & Time Off Breadth — Paid vacation, holidays, sick leave, volunteer time, and parental leave are called out, alongside flexible or remote work options. Unlimited PTO is mentioned in some contexts.
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Retirement Support — Savings and retirement elements appear across regions, including a 401(k) in the U.S. and a savings fund and profit sharing in Mexico. These components provide structured long-term financial benefits.
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The Company
What We Do
Wizeline, a global technology services provider, builds the best digital products and platforms at scale. We focus on measurable outcomes, partnering with our customers to modernize core technologies, mature data-driven capabilities, and improve user experience.
