AI​/ML Engineer

About the position

We’re seeking a Senior ML Engineer to build next-generation AI systems that help millions of patients access care faster. You’ll architect production ML infrastructure handling thousands of hours of service interactions daily in a highly regulated healthcare environment. This is a high-impact individual contributor role—ideal for someone eager to “own the outcome” and push the boundaries of “high tech + high touch” care experiences.

• Architect and build large-scale AI systems that integrate high-volume voice, text, and contextual event streams with extensive knowledge bases to deliver real-time recommendations, automations, and decision support.

• Design and operate workflow-oriented AI systems, including DAG-based execution graphs, stateful pipelines, and agent-driven workflows with clear observability, reproducibility, and fault tolerance.

• Build agent architectures spanning agent-to-agent coordination, feedback loops, tool‑calling systems, and long‑running autonomous workflows, balancing control, safety, and adaptability.

• Design and implement data models, feature pipelines, and APIs to support model training, low‑latency inference, and continuous learning.

• Develop predictive, real‑time analytics systems that combine streaming data, ML inference, and event‑driven triggers to surface insights and automate actions at scale.

• Implement and maintain end‑to‑end ML platforms, including model training, evaluation, deployment, online inference, monitoring, and drift detection.

• Partner closely with product managers, data scientists, and QA engineers to translate experimental models into reliable, production‑grade AI services.

• Identify, diagnose, and resolve performance and scaling bottlenecks across data pipelines, inference services, and orchestration layers as production workloads grow.

• 5+ years of software engineering experience, with 3+ years focused on machine learning or applied AI systems.
• Strong proficiency in Python, particularly for ML pipelines, frameworks, inference services, and APIs (e.g., scikit‑learn, Sanic API, PyTorch Lightning, Pydantic AI, Lang Graph, Bedrock, OpenAI / Anthropic SDKs).
• Experience designing ML‑centric data architectures, including feature stores, vector databases, and time‑series systems for monitoring and analytics.
• Hands‑on experience with cloud‑native inference: containerized model serving, autoscaling, GPU/accelerator workloads, and low‑latency production deployments.
• Experience operating end‑to‑end MLOps platforms (e.g., MLflow, Kubeflow), including CI/CD for models, experiment tracking, and rollout strategies.
• Solid understanding of workflow orchestration (graph‑based execution, retries, state management) in ML and agent‑based systems.
• Excellent communication skills, with the ability to collaborate effectively across engineering, product, and non‑technical stakeholders.
• Strong interest in healthcare innovation and building AI systems that meaningfully improve health outcomes.
• Working knowledge of AI safety, bias detection, and responsible AI practices.

• Experience building AI systems in healthcare or regulated environments, with familiarity with standards such as HIPAA, GDPR, or FDA guidance.
• Proven experience leading complex technical initiatives and mentoring junior engineers.
• Strong applied knowledge of event‑driven architectures and streaming systems (Kafka, Pub/Sub, Kinesis, RabbitMQ).
• Hands‑on experience designing and operating vector search, RAG pipelines, and hybrid retrieval systems.
• Experience with agent frameworks, multi‑agent coordination patterns, and long‑running agent loops in production environments.
  Familiarity with real‑time analytics stacks combining streaming data, ML inference, and operational dashboards.