What You'll Do
Lead the development of production-grade machine learning systems, guiding projects from concept through deployment and optimization. Design and maintain scalable ML infrastructure that supports complex workflows across multiple domains. You'll be responsible for improving model performance, reliability, and efficiency while ensuring code quality and maintainability.
Collaborate with data engineers, DevOps, and solutions architects to integrate ML components into broader data ecosystems. Play a key role in architectural discussions, contribute to internal best practices, and help shape the future of our machine learning capabilities. Share expertise through documentation, technical reviews, and knowledge-sharing sessions.
Requirements
- Strong foundation in machine learning principles, including supervised, unsupervised, and reinforcement learning techniques
- Proven experience building and deploying deep learning models, especially using frameworks like TensorFlow or PyTorch
- Hands-on work with transformer architectures, LLM-based applications, and retrieval-augmented generation systems
- Proficiency in prompt engineering and evaluating LLM outputs using appropriate metrics
- Expertise in Python, pandas, numpy, and SQL for data processing and model development
- Experience designing ETL/ELT pipelines and working with distributed computing tools like Spark or Dask
- Familiarity with vector databases and embedding models in production settings
- Production deployment experience using Docker, CI/CD pipelines, and model monitoring tools
- In-depth knowledge of cloud platforms, particularly AWS and GCP, including services like SageMaker, Lambda, and S3
- Experience with infrastructure-as-code tools such as Terraform or CloudFormation
- Track record of using experiment tracking platforms like MLflow or Weights and Biases
Benefits
- Fully remote working environment with long-term B2B engagement
- Financial support for medical insurance
- Paid time off including vacation, sick leave, and public holidays
- Continuous learning opportunities and full sponsorship for AWS certifications
- Active participation in a culture focused on innovation, knowledge sharing, and technical growth
