Our Unbabel division is seeking a motivated AI Engineer to join our AI Productization Team, helping scale and deliver cross-platform language solutions, including translation and customer support automation, to enterprise partners.
Are you interested in solving technical challenges across complex software systems? Do you enjoy building robust integrations that connect diverse platforms and workflows? As part of a collaborative team, you’ll contribute to the development, maintenance, and expansion of core AI services that power reliable, high-quality language solutions.
This role is ideal for someone looking to grow their engineering skills by building new integrations, improving system performance, and contributing to the rollout of new AI-driven capabilities.
Responsibilities:
- Lead applied research initiatives to solve challenging problems in translation and customer support automation
- Design, implement, evaluate, and optimize ML and NLP models for real-world machine translation use cases
- Work with large-scale, multilingual datasets to build accurate and scalable systems
- Develop, optimize, and maintain AI services and supporting infrastructure in production environments
- Contribute to the development of data pipelines and computing infrastructure that support efficient machine translation workflows
Must Haves:
- 2+ years of relevant industry experience
- Strong understanding of applied AI and machine learning fundamentals
- Proven ability to write clean, maintainable, and well-tested code
- Experience building and consuming APIs (REST and/or gRPC)
- Familiarity with relational and non-relational databases (e.g., PostgreSQL, MongoDB)
- Experience developing and maintaining production systems, with attention to reliability and security
- Experience working in Agile, collaborative development environments
- Professional proficiency in English (written and spoken)
Nice to Haves:
- Experience with Python web frameworks such as FastAPI or Flask
- Experience deploying and operating LLMs or model serving systems
- Experience owning AI services in production, including monitoring, alerting, and performance tuning
- Hands-on experience with observability tools (e.g., Prometheus, Grafana, PagerDuty)
- Exposure to cloud and DevOps practices (AWS, CI/CD, infrastructure as code such as Terraform)
- Familiarity with microservices architecture and container orchestration (e.g., Kubernetes)
- Contributions to ML/NLP projects (professional or personal) demonstrating practical AI engineering skills