Plenful is looking to transform healthcare operations by building an AI agentic operating platform that helps healthcare teams operate smarter, faster, and more efficiently, reducing administrative burden and improving compliance.
Requirements
- Expertise in Python for backend services and ML integration.
- Strong applied ML experience in a SaaS context—NLP pipelines, LLM fine-tuning/evaluation, or healthcare ML applications.
- Familiarity with ML frameworks such as PyTorch, TensorFlow, scikit-learn, or Hugging Face Transformers.
- Hands-on experience with cloud infrastructure (AWS: S3, Lambda, ECS, SQS, CloudWatch).
- Proficiency in relational databases (PostgreSQL) and designing schemas to support ML applications.
- Experience deploying ML models into production systems, not just research or prototyping.
- Solid understanding of CI/CD pipelines, Git workflows, and automated testing for ML/Backend systems.
Responsibilities
- Design, build, and maintain backend services and APIs in Python, with a focus on integrating and deploying machine learning models.
- Architect scalable infrastructure on AWS (S3, Lambda, SQS, ECS, CloudWatch) to support ML pipelines and inference at scale.
- Collaborate with product managers, designers, data scientists, and engineers to define and deliver ML-powered product features.
- Deploy, monitor, and optimize ML models in production, ensuring performance, security, and compliance with HIPAA and other healthcare regulations.
- Develop and optimize data pipelines for training, inference, and real-time decision-making.
- Implement best practices for CI/CD, testing (Pytest), and version control (Git) to ensure reliable ML feature deployment.
- Troubleshoot and resolve production issues across ML services, APIs, and infrastructure.
Other
- 3+ years of professional backend development experience, with a Bachelor’s degree in Computer Science, Machine Learning, or a related field.
- Proven track record of owning projects end-to-end in a fast-paced startup or B2B SaaS environment.
- Experience in regulated industries (healthcare, HIPAA compliance) preferred.
- Strong communication skills with the ability to translate ML concepts into product outcomes.
- Provide technical leadership—mentoring engineers, reviewing code, and championing backend + ML integration best practices.
