We are seeking a Senior Machine Learning Engineer to lead the development of advanced AI systems that power intelligent care delivery. In this role, you'll design and implement large-scale, production-ready machine learning platforms that process vast streams of voice, text, and event data to generate real-time insights and automated decision support for healthcare services.
Key Responsibilities
- Design and deploy AI architectures that integrate streaming data, knowledge bases, and contextual signals to deliver low-latency recommendations and automations.
- Build and maintain stateful, workflow-driven systems using directed acyclic graphs (DAGs), agent coordination patterns, and tool-integrated loops with strong observability and fault recovery.
- Develop robust data models, feature engineering pipelines, and APIs that support continuous training, inference, and model lifecycle management.
- Construct real-time analytics engines that combine event triggers, ML predictions, and operational workflows to enable scalable automation.
- Own the end-to-end MLOps stack, from model training and evaluation to deployment, monitoring, and drift detection in production environments.
- Collaborate with product, data science, and QA teams to transition experimental models into reliable, auditable AI services.
- Diagnose and resolve performance bottlenecks across distributed inference systems, data pipelines, and orchestration layers as system demands grow.
Qualifications
- 5+ years of software engineering experience, with at least 3 years focused on machine learning or applied AI systems.
- Strong command of Python and ML frameworks such as PyTorch Lightning, scikit-learn, LangGraph, and SDKs for OpenAI, Anthropic, or AWS Bedrock.
- Proven experience building ML data architectures, including feature stores, vector databases, and time-series monitoring systems.
- Hands-on expertise in cloud-based model serving, containerized inference, GPU acceleration, and low-latency deployment patterns.
- Familiarity with MLOps platforms like MLflow or Kubeflow, including CI/CD for models, experiment tracking, and rollout strategies.
- Understanding of workflow orchestration, state management, and fault-tolerant execution in agent-based systems.
- Excellent communication skills and the ability to work across technical and non-technical teams.
- Passion for healthcare innovation and a commitment to building AI systems that improve patient outcomes.
- Knowledge of AI safety, bias mitigation, and responsible AI principles in practice.
Preferred Experience
- Experience in healthcare or other regulated domains, with awareness of compliance standards such as HIPAA or GDPR.
- Track record of leading technical initiatives and guiding junior engineers.
- Proficiency with event-driven systems like Kafka, Pub/Sub, Kinesis, or RabbitMQ.
- Experience building RAG pipelines, hybrid search systems, and vector retrieval infrastructures.
- Production-level work with agent frameworks, multi-agent coordination, and long-running autonomous workflows.
- Familiarity with real-time analytics stacks that combine streaming data, ML inference, and operational dashboards.
Technology Environment
Our stack centers on Python-driven ML pipelines with tools including scikit-learn, Sanic API, Pydantic AI, and LangGraph. We use cloud-native inference with GPU support, containerized serving, and orchestration via DAG-based and stateful workflows. Our data infrastructure includes vector databases, feature stores, and time-series systems, with streaming layers powered by Kafka, Kinesis, or Pub/Sub. MLOps is managed through MLflow and Kubeflow, enabling scalable model deployment and monitoring.
Work Environment
This role operates in a collaborative, cross-functional setting that values ownership, innovation, and responsible AI. We combine technical rigor with a mission-driven focus on creating high-impact care experiences. Engineers are expected to lead initiatives, solve complex challenges, and contribute to a culture of continuous learning and ethical AI development.
