Artificial Intelligence Product Lead

1 week ago

Copenhagen, Copenhagen, Denmark Languagewire AS Full time

About the Role

Languagewire A/S is seeking an experienced Artificial Intelligence Product Lead to spearhead our AI/NLP initiatives. As a key member of our team, you will be responsible for leveraging advanced AI and NLP technologies to enhance our product offerings and expand our capabilities.

Key Responsibilities

  • You will play a critical role in ensuring that our AI innovations are integrated into intuitive products and made available through reliable and robust APIs.
  • As a technical leader, you will provide guidance and direction to your team, fostering collaboration and innovation.
  • You will work closely with cross-functional teams to develop and implement cutting-edge AI solutions.
  • You will oversee the development of neural models for machine translation and content generation.
  • You will ensure the seamless integration of AI capabilities into our products and expose them through powerful APIs.
  • You will take ownership of the AI services lifecycle within your team.
  • You will advocate for a culture of end-to-end ownership and continuous improvement.
  • You will apply data-driven approaches to decision-making, utilizing metrics and feedback to guide product development and API strategy.
  • You will lead the charge in engineering groundbreaking AI solutions, pushing the boundaries of what's possible in the language industry.

Requirements

  • Expert knowledge in Python, especially using frameworks like Huggingface Transformers, TensorFlow or PyTorch, Scikit-learn.
  • Experience with neural models based on Transformer architectures.
  • Expert knowledge in JavaScript/TypeScript for full-stack development and interaction with APIs on the front end.
  • API Development: RESTful API principles, understanding how to design stateless, secure, and scalable APIs.
  • Expertise in MLOps Tools - Docker: For containerizing applications and models.
  • Kubernetes: For orchestration of containers.
  • CI/CD Tools (e.g., Jenkins, GitLab CI, GitHub Actions): For continuous integration and deployment pipelines.
  • Terraform (IaC).
  • MLflow or Kubeflow: For managing the machine learning lifecycle.
  • Cloud Platforms: Azure, AWS, Google Cloud
  • Data Management: SQL/NoSQL
  • Data Lakes/ Data Warehousing: Solutions like Amazon S3, Redshift, or Google BigQuery. Apache Kafka or other streaming services for real-time data processing.
  • Redux or Context API: For state management in front-end applications.
  • Version Control and Collaboration: Git: For source code management. GitHub, Azure DevOps: For collaborative development and code reviews.
  • Monitoring and Logging: Prometheus, Grafana, or ELK Stack (Elasticsearch, Logstash, Kibana) for monitoring applications and infrastructure.
  • Security: OAuth, JWT, and other authentication/authorization protocols. Best practices are adopted.
  • Data Visualization and BI Tools: Looker, Tableau, Power BI or similar tools for data analysis and visualization.
  • Agile methodologies and understanding of design workflows and software architecture.

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