job description
Join Shearwater Health as an ML/Integration Engineer and be at the forefront of AI innovation. In this dynamic role, you'll design, develop, and deploy cutting-edge machine learning models that power intelligent enterprise applications. Collaborate with cross-functional teams to integrate AI solutions seamlessly into existing systems, ensuring scalability, performance, and real-world impact.
Based in the vibrant tech hubs of Bali, you'll work in a fast-paced, agile environment where your contributions will directly shape the future of healthcare and enterprise AI. Whether you're optimizing algorithms, building robust APIs, or ensuring seamless system integration, your work will drive meaningful change.
If you're passionate about AI, thrive in collaborative settings, and want to make a tangible difference, this is your opportunity to grow with a forward-thinking company.
Responsibility
- Design, develop, and deploy scalable machine learning models for enterprise applications.
- Integrate AI solutions with existing systems, ensuring seamless functionality and performance.
- Collaborate with data scientists, engineers, and product teams to refine and optimize ML pipelines.
- Develop and maintain APIs and microservices to support AI-driven features.
- Monitor and improve model performance, accuracy, and efficiency in production environments.
- Implement best practices for data preprocessing, feature engineering, and model validation.
- Document technical processes, architectures, and integration workflows for team alignment.
- Stay updated with emerging AI/ML trends and technologies to drive innovation.
Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
- Proven experience in machine learning, AI integration, or software engineering.
- Proficiency in Python, TensorFlow, PyTorch, or similar ML frameworks.
- Strong understanding of RESTful APIs, microservices, and cloud platforms (AWS, GCP, Azure).
- Experience with data pipelines, ETL processes, and big data technologies.
- Familiarity with CI/CD pipelines and DevOps practices for ML deployment.
- Excellent problem-solving skills and attention to detail.
- Strong communication skills and ability to work in cross-functional teams.