Lead Analyst - Epic AI integration (NGEMR)

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Date: 25 Aug 2026

Location: SG

Company: Synapxe

Position Overview

The Epic Nebula AI / Machine Learning Engineer is responsible for designing, deploying, integrating, and supporting artificial intelligence and machine learning solutions within the Epic ecosystem. Working closely with clinicians, operational stakeholders, data scientists, Epic application teams, integration teams, security teams, and infrastructure teams, the role translates business and clinical requirements into safe, reliable, and scalable AI-enabled workflows. The engineer deploys predictive and machine learning models through Epic Nebula and the Epic Cognitive Computing framework, integrates model inputs and outputs into Epic workflows, and ensures that all deployed models meet organizational standards for performance, security, privacy, explainability, clinical safety, and ongoing monitoring. This role requires strong technical expertise in machine learning engineering, complemented by a deep understanding of healthcare workflows, Epic applications, clinical data, and production support processes.

Role & Responsibilities

  • Experience leading end-to-end AI deployment projects from use-case assessment through production implementation.
  • Ability to define AI architecture, deployment standards, validation frameworks, and operational support models.
  • Experience reviewing model design and challenging unsafe or technically weak assumptions.
  • Ability to determine whether a model is sufficiently accurate, stable, explainable, and operationally useful for deployment.
  • Experience managing vendor-developed models and assessing vendor technical claims.
  • Ability to mentor junior engineers, analysts, and data scientists.
  • Experience presenting AI risks, performance results, and implementation recommendations to governance committees and senior management.
  • Meeting with clinicians, business stakeholders, Epic application analysts, data scientists, and subject matter experts to understand AI and predictive analytics requirements.
  • Evaluating whether proposed use cases are suitable for machine learning, generative AI, rules-based logic, or existing Epic functionality.
  • Translating clinical and operational requirements into technical model-integration specifications.
  • Preparing, validating, and transforming Epic clinical and operational data for model development and inference.
  • Working with Epic application teams to configure user-facing components such as alerts, decision-support activities, work queues, patient lists, dashboards, or other workflow touchpoints.
  • Validating model compatibility, dependencies, input schemas, output schemas, and runtime requirements.
  • Assessing model accuracy, calibration, sensitivity, specificity, false-positive rates, false-negative rates, fairness, and operational impact.
  • Establishing monitoring for model availability, latency, data quality, prediction distribution, model drift, and workflow adoption.
  • Participating in Epic upgrades and reviewing changes that may affect Nebula, cognitive computing, data structures, interfaces, or embedded AI workflows.
  • Ensuring compliance with organizational policies relating to cybersecurity, patient privacy, clinical safety, responsible AI, and data governance.
  • Supporting periodic model review, recalibration, retraining, rollback, retirement, and replacement.

Requirements

  • A degree in computer science, data science, artificial intelligence, engineering, statistics, information systems, biomedical informatics, or a related discipline.
  • Strong programming skills in Python or another language commonly used for machine learning development.
  • Proficiency with SQL and relational database concepts.
  • Practical understanding of machine learning development, validation, deployment, and monitoring.
  • Experience working with model-serving APIs, JSON, RESTful services, authentication, and system integration.
  • Knowledge of software development lifecycle, version control, automated testing, release management, and production support.
  • Understanding of model performance measures such as precision, recall, sensitivity, specificity, calibration, and area under the curve.
  • Ability to identify data leakage, bias, model drift, unstable features, and inappropriate model use.
  • Strong analytical, troubleshooting, and problem-solving skills.
  • Strong written and verbal communication skills.
  • Ability to explain technical model behaviour and limitations to clinical and non-technical stakeholders.

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