Senior Healthcare Artificial Intelligence (AI) Engineer, Analytics Hub

ECG Management Consultants•Boston, MA

4d•Hybrid

About The Position

ECG, a leading healthcare consulting firm with over 50 years of experience, is seeking a Senior Healthcare Artificial Intelligence (AI) Engineer to join its growing Analytics Hub. This role is crucial in driving the firm's AI strategy by designing, developing, and deploying intelligent solutions that enhance client delivery, accelerate internal operations, and create new service offerings. The engineer will work at the intersection of healthcare, data science, software engineering, and generative AI, transforming complex challenges into scalable AI-powered applications. This position offers a dynamic work environment with diverse projects and opportunities for continuous learning and support.

Requirements

Strong knowledge of and passion for the healthcare industry
Bachelor’s degree in computer science, computer engineering, mathematics, statistics, health information management, or a related degree that relies heavily on critical thinking, logic, and math
5+ years of professional experience working in data engineering or data science
2+ years of recent experience developing production AI, ML, or advanced analytics solutions
Strong proficiency in: Python, SQL, and ML frameworks (TensorFlow, PyTorch, scikit-learn)
Strong proficiency in Microsoft Azure AI Services, Azure OpenAI, AWS, or Google Cloud AI; strong preference for Azure AI Services
Experience developing and deploying LLM-based applications
Experience with agentic AI frameworks and LLM evaluation frameworks
Knowledge of MLOps, CI/CD pipelines, containerization (Docker), and cloud architecture
Strong analytical, problem-solving, and communication skills
Having the permanent legal right to work in the United States is a condition of employment.

Nice To Haves

A master's degree is a plus
Healthcare or consulting experience is preferred
Work with healthcare data standards including HL7, FHIR, DICOM, ICD-10, CPT, and revenue cycle data
Experience working with healthcare data, healthcare technology, healthcare consulting, health systems, payer organizations, or digital health companies
Experience deploying agents from Azure AI Foundry and/or Microsoft Copilot Studio
Experience using the Microsoft Fabric platform

Responsibilities

Design and develop production-grade generative AI, ML, and agentic AI solutions for healthcare and business applications.
Collaborate with IT and Hub members to deploy solutions.
Build retrieval-augmented generation systems, intelligent assistants, AI agents, and workflow automation solutions using enterprise and healthcare data.
Develop evaluation frameworks that measure solution quality, accuracy, groundedness, reliability, and business impact.
Contribute to the creation and improvement of automated testing methodologies, benchmark datasets, and regression testing processes to ensure AI solutions remain stable as prompts, models, and data sources evolve.
Partner with business stakeholders (consultants, clients, business development, HR, etc.) to define success metrics and acceptance criteria for AI-powered applications.
Evaluate emerging AI technologies and recommend architectures that balance performance, cost, scalability, and risk.
Deploy and operationalize AI solutions within secure enterprise and client environments.
Design scalable architectures that support customer-facing AI applications, agentic workflows, and intelligent automation solutions.
Build and maintain CI/CD processes, monitoring capabilities, and deployment pipelines for AI applications.
Implement observability, telemetry, and performance monitoring to track quality, adoption, and operational health.
Collaborate with IT, cybersecurity, and governance teams to ensure secure deployment and compliance with firm requirements.
Ensure AI systems comply with HIPAA, security, privacy, and regulatory requirements (e.g., SOC2, HITRUST), where applicable.
Contribute to AI governance practices around model monitoring, fairness, transparency, explainability, and risk mitigation.
Collaborate with cybersecurity and data governance teams to ensure secure deployment of AI solutions.
Partner with division leaders, healthcare consultants, clinicians, and operational stakeholders to identify AI opportunities.
Translate business requirements into technical AI solutions.
Present findings, recommendations, and system capabilities to both technical and nontechnical audiences.
Stay current with emerging AI technologies, healthcare innovation, and industry best practices.