Generative AI Engineer About Apertera Apertera is leading the evolution of language solutions for high-stakes content.
We partner with enterprises as an extension of their teams, combining professional expertise with Adaptive AI technology that is continuously refined by client context.
For more than twenty years, Apertera has set the bar for legal, financial, and regulatory translation, serving the most rigorous buyers, including over 75% of major national Canadian law firms, all major banks, and leading securities regulators.
Apertera is Canadian-owned, ISO 17100 and SOC 2 certified.
Our core values:
Innovation Dedication Fanatical commitment to quality and service Resourcefulness Collaboration About the Role We are looking for a Generative AI Engineer to develop our next-generation intelligent translation and translation-related service engine, using Generative AI (GenAI) and Large Language Model (LLM) technologies.
You will report to the team lead in AI Innovation, develop and implement state-of-the-art algorithms by fast prototyping, and collaborate with the software team to deploy models.
We expect our Generative AI Engineer to to work at the intersection of LLM engineering, machine translation, cloud infrastructure, and evaluation.
You'll play a pivotal role in pushing the boundaries of applying GenAI to translation scenarios and create innovative solutions.
Responsibilities Implement state-of-the-art LLM techniques including continued pre-training, instruction fine-tuning, preference alignment, and LLM deployment.
Work closely with machine learning engineers and data engineers to design, build, and test models.
Develop efficient and scalable algorithms for training and inference of generative models, leveraging deep learning frameworks such as Tensor Flow or PyTorch and optimizing performance on diverse hardware platforms.
Train and evaluate generative models using appropriate metrics and benchmarks, fine-tuning model parameters, architectures, and hyperparameters to optimize performance, stability, and generalization.
Built end-to-end prototypes that are production ready.
Work closely with software and Dev Ops engineers to deploy GenAI models. Document code, algorithms, and experimental results, following best practices for reproducibility, version control, and software engineering, and contributing to internal knowledge sharing and continuous improvement initiatives.
Requirements Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, or related fields.
A Master’s degree is preferred.
Proficiency in Python programming and software development practices, with experience in building and maintaining scalable, production-grade software systems.
Working knowledge and project-based record of all of the following: context engineering, RAG, harness engineering.
Working knowledge and project-based record of at least one of the following is a plus: LLM post-training, APO, agentic workflow.
Strong problem-solving skills, attention to detail, and the ability to work independently and collaboratively in a fast-paced environment.
Hands-on experience with Huggingface APIs or Amazon Bedrock. Expert skills of Python, including PyTorch, Tensor Flow, Pandas, etc.
Experience with cloud platforms like AWS, GCP, or Azure Excellent problem-solving skills, critical thinking, and the ability to work independently and collaboratively in a fast-paced environment.
Strong communication skills, with the ability to articulate complex technical concepts effectively and work cross-functionally with diverse teams.
Self-driven, self-motivated with excellent time management skills Excellent organizational, communication, and interpersonal skills Ability to adapt to shifting priorities without compromising deadlines and momentum.
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