About the Role
The role involves developing and deploying machine learning models with a focus on generative AI, leveraging AWS platforms and tools to build scalable solutions.
Responsibilities
- Design and implement machine learning pipelines for generative AI applications
- Develop and optimize models using AWS SageMaker and related services
- Collaborate with data scientists to integrate AI models into production systems
- Monitor model performance and implement improvements
- Ensure solutions are scalable and maintainable in cloud environments
- Work with large datasets to train and validate AI models
- Apply best practices in software engineering to ML development
- Troubleshoot and resolve technical issues in AI systems
- Participate in code reviews and system design discussions
- Support deployment and CI/CD processes for ML models
Nice to Have
- Experience with large language models or diffusion models
- Background in natural language processing or computer vision
- Hands-on experience with MLOps tools and practices
- Knowledge of Kubernetes and orchestration platforms
- Prior work with real-time inference systems
- Familiarity with data annotation pipelines
- Contributions to open-source ML projects
- Experience in agile development environments
Benefits
- Flexible working hours
- Remote work policy
- Professional development opportunities
- Access to training and certification programs
- Health and wellness support
- Paid time off and holidays
- Modern tech stack and tools
- Supportive team environment
- Opportunities for career advancement
- Inclusive company culture
Compensation
Competitive salary based on experience and qualifications
Work Arrangement
Remote
Team
Collaborative team focused on artificial intelligence and machine learning solutions
Technologies We Use
- AWS SageMaker, EC2, S3, Lambda, and Step Functions
- Python, PyTorch, Hugging Face Transformers
- Docker, Kubernetes, Terraform
- MLflow, Airflow, Prometheus
Our Approach to AI Projects
- Focus on ethical AI development and responsible deployment
- Iterative prototyping and validation cycles
- Close collaboration between engineers and data scientists
- Emphasis on model interpretability and monitoring
Available for qualified candidates
