Senior Machine Learning Engineer (Remote in Portugal possible)

The Senior Machine Learning position at Language Wire is an opportunity for anyone deeply passionate about AI/NLP, and Machine Translation (MT). The role is notable for offering the chance to spearhead the development of next generation neural models, positioning an individual at the forefront of advancements in language processing technology. It's not just about technical expertise; it involves driving initiatives that have the potential to dismantle language barriers globally, offering significant professional growth and the satisfaction of contributing to a more interconnected world. Moreover, the challenges faced in improving translation accuracy, handling diverse languages, and ensuring cultural nuance preservation make this position uniquely rewarding for those eager to innovate and excel in the dynamic field of AI/NLP, and Machine Translation.

Does this role seem interesting? Yes? You should read on!

The team you’ll be a part of

The successful candidate will join a dynamic, cross-functional team at the forefront of AI/NLP and Machine Translation, dedicated to revolutionising language services. As a Senior Machine Learning Engineer, you will collaborate closely with Full-Stack Engineers, QA Engineers, Machine Learning Engineers, Data Engineers, Product Manager and report to the ML Team Lead.

If you want to make a difference, make it with us by…

• Develop innovative next generation neural models for Machine Translate, transforming how languages are learned, processed, and understood on a global scale.
• Ensure that our machine translation and next gen AI services are not only innovative but also reliable, scalable, available, and delivered fast to meet the demands of clients. 
• Keep abreast of the latest trends and advancements in machine learning, natural language processing, and machine translation technologies. By engaging in ongoing research, experimentation, and adoption of novel methodologies, you will drive innovation within the team.
• Additionally, you will share your expertise and insights through mentorship, workshops, and technical presentations, fostering a culture of knowledge sharing and continuous improvement. Advocate for the “you build it, you run it" philosophy.

In one year, you'll know you were successful if...

•  You have successfully navigated through the challenges of integrating AI technologies into user-friendly products, as evidenced by positive feedback from internal and external stakeholders.
• You have established robust monitoring and prediction services that support the continuous improvement and scalability of our product offerings.
• You have introduced innovative machine translation and content generation tools that have measurably improved efficiency and customer satisfaction.
• You have cultivated a culture of excellence within your team, promoting collaboration and innovation.
• You have successfully developed a training/fine-tuning pipeline for self-hosted LLMs - both for Machine Translate (MT) & Gen AI
  

Desired experience and competencies

What does it take to work for LanguageWire?

What you’ll need to bring

• Expert Knowledge in Python and especially using frameworks like Huggingface Transformers, TensorFlow or PyTorch, PyTorch-Lightning, Scikit-learn.
• Experience with neural models based on Transformer architectures.
• Proven experience in training sophisticated neural networks on extensive NLP datasets, harnessing the power of multiple GPUs.
• Proficiency in developing and fine-tuning multilingual models to handle diverse language pairs effectively.
• Awareness of ethical considerations and biases in machine translation systems, along with strategies to mitigate them, demonstrating a commitment to responsible AI development.
• Experience with Retrieval Augmented generation (RAGs) and Vector DBs.
• Experience and competency in managing and interfacing with SQL and NoSQL databases, e.g. BigQuery
• Comprehensive knowledge of CI/CD workflows and terraform (IaC), with a specialized focus on LLM operations, MLOps.
• MLflow or Kubeflow: For managing the machine learning lifecycle.
• Containerization skills with Docker and Kubernetes.
• Proficiency in cloud platforms: Azure, Google Cloud, or AWS.
• Version Control and Collaboration - Git: For source code management. GitHub, Azure DevOps: For collaborative development and code reviews.
• Ability to optimize AI applications for performance, scalability, and security.
• Data Visualization and BI Tools: Looker, Power BI,  Tableau or similar tools for data analysis and visualization.
• Monitoring and Logging: Prometheus, Grafana, or ELK Stack (