Machine Learning Solutions Architect
1 week ago
About the Company
Languagewire A/S is a leading provider of AI-powered language solutions. Our mission is to wire the world together with language, making communication easier for people and businesses across the globe.
Our Values
- We are curious and intrigued by what we don't understand.
- We believe relationships are based on honesty and responsibility.
- We are caring and respect each other personally and professionally.
- We encourage authentic collaboration, invite feedback, and a positive social environment.
About the Job
As a Machine Learning Solutions Architect at Languagewire A/S, you will be responsible for designing and implementing cutting-edge AI solutions that meet the needs of our clients.
Key Responsibilities
- You will work closely with cross-functional teams to develop and implement 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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