AI / ML Engineer (HealthTech)

The client is a healthcare technology company on a mission to improve outcomes through data-driven insights. With a focus on delivering industry-leading Patient-Reported Outcome Measures (PROMs) adoption rates, the company empowers providers with tools for smarter operations and better care. Their current AI maturity presents a greenfield opportunity for technical leaders eager to define and implement strategic AI solutions in a collaborative and fast-paced environment.

Project Description

The project centers around developing a suite of AI-powered tools within a healthcare platform. These tools aim to enhance patient engagement, automate communication via sentiment-aware CRM integration, process unstructured device data using GenAI, and surface predictive analytics to detect early signs of patient risk. Additionally, a natural language-driven insights interface enables dashboard generation and anomaly detection to improve care quality, reduce operational costs, and increase platform engagement.

Technologies

• Python
• FastAPI
• PyTorch
• TensorFlow
• OpenAI
• Bedrock
• Docker
• AWS (ECR, ECS, EC2, Lambda)
• CI / CD
• MLflow
• SQL
• NoSQL
• OpenCV
• Kafka
• AWS SageMaker
• GCP

What You'll Do

Architect, develop, and deploy ML solutions aligned with cost-reduction and revenue-growth KPIs;

Own the full ML lifecycle including data acquisition, model training, evaluation, A / B testing, and production deployment;

Translate business objectives into robust technical solutions in collaboration with stakeholders;

Refactor and modernize legacy components into scalable infrastructure;

Ensure high code quality through strong testing and observability practices;

Lead demos and workshops for business and technical audiences;

Job Requirements

Minimum 3+ years of experience in machine learning or data engineering roles;

Solid theoretical foundation in machine learning, natural language processing (NLP), and computer vision;

Strong Python development skills and experience building end-to-end ML / NLP / CV pipelines in production environments;

Proven experience deploying ML systems in cloud environments, preferably AWS (ECR, ECS, EC2, Lambda, Bedrock);

Hands-on experience with containerization tools like Docker and orchestration in production settings;

Familiarity with distributed systems and message-based architectures; Kafka experience is a plus;

Experience setting up and maintaining CI / CD pipelines and version control workflows using modern DevOps practices;

What Do We Offer

The global benefits package includes :

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