Senior ML Engineer

Harnham; Boston (MA); On-site; USD 140,000 - 190,000; Full time

Job summary

Harnham is seeking a Senior Machine Learning Engineer to expand enterprise AI capabilities across healthcare technology. The role combines ML engineering, MLOps, cloud infrastructure, and generative AI operations to deploy scalable production systems.

The position emphasizes reliability, observability, and measurable business outcomes, with collaboration across data science teams to productionise models, monitor performance, and optimize prompts and workflows.

Qualifications

• 6+ years of Machine Learning Engineering experience.
• Proven experience deploying machine learning models into production.
• Experience with AWS cloud services and ML platforms such as SageMaker.
• Experience using cloud-based LLM and foundation model services.
• Experience with DBT and data transformation pipelines.
• Experience with workflow orchestration tools (Prefect or Airflow).
• Healthcare industry experience, including regulated environments and security requirements.
• Responsible AI considerations, including fairness and bias mitigation.

Responsibilities

• Design, build, and maintain production-grade ML systems.
• Deploy and support ML models in live environments.
• Develop scalable training and inference workflows.
• Improve system reliability, performance, and operational efficiency.
• Partner with data science teams to productionise models and analytics solutions.
• Develop and maintain feature engineering pipelines.
• Build and enhance workflow orchestration solutions.
• Establish best practices for model deployment and lifecycle management.
• Implement monitoring, observability, and alerting frameworks.
• Support model governance and regulatory compliance requirements.

Skills

Advanced SQL

AWS cloud services

SageMaker

Cloud-based LLM services

DBT

Workflow orchestration (Prefect/Airlf​

MLOps & Infrastructure

Monitoring/observability

Feature engineering pipelines

Healthcare compliance awareness

Tools

SageMaker

Prefect

Airflow

DBT

AWS

Job description

Overview

A growing healthcare-focused technology organisation is seeking a Senior Machine Learning Engineer to help expand and support enterprise AI and machine learning capabilities. This role combines machine learning engineering, MLOps, cloud infrastructure, and generative AI operations.

The ideal candidate has extensive experience deploying, monitoring, and scaling production machine learning systems and is comfortable supporting both traditional ML models and modern large language model (LLM) applications.

This position is focused on operational excellence, reliability, scalability, and measurable business outcomes rather than research.

Key Responsibilities

• Machine Learning Engineering & Platform Development


• Design, build, and maintain production-grade machine learning systems.


• Deploy and support machine learning models in live environments.


• Develop scalable training and inference workflows.


• Improve system reliability, performance, and operational efficiency.


• Partner with data science teams to productionise models and analytics solutions.


• MLOps & Infrastructure


• Develop and maintain feature engineering pipelines.


• Build and enhance workflow orchestration solutions.


• Establish best practices for model deployment and lifecycle management.


• Implement monitoring, observability, and alerting frameworks.


• Support model governance and operational compliance requirements.


• Generative AI & LLM Operations


• Deploy and maintain applications powered by large language models.


• Monitor model performance, quality, and reliability.


• Optimise prompts, inference processes, and operational workflows.


• Support cloud-hosted foundation model services.


• Implement improvements that enhance model effectiveness and user experience.


• Business Impact


• Contribute to predictive and decision-support solutions within healthcare and life sciences domains.


• Support projects involving optimisation, forecasting, classification, and recommendation systems.


• Deliver scalable solutions that create measurable value for end users and stakeholders.

Required Qualifications

• Technical Skills


• Advanced SQL proficiency.


• Hands-on experience with AWS cloud services.


• Experience with machine learning platforms such as SageMaker.


• Experience using cloud-based LLM and foundation model services.


• DBT experience.


• Workflow orchestration experience with Prefect or similar platforms such as Airflow.


• Experience


• Approximately 6+ years of Machine Learning Engineering experience.


• Proven experience deploying machine learning models into production.


• Experience supporting model training and inference workloads.


• Strong model monitoring and observability experience.


• Ownership of MLOps processes and production ML environments.


• Experience designing and maintaining feature engineering pipelines.


• Infrastructure-focused engineering mindset with strong software engineering practices.


• Healthcare industry experience is required, including familiarity with:


• Regulated environments.


• Compliance and security requirements.


• Responsible AI considerations, including fairness and bias mitigation.


• Preferred Qualifications


• Experience building ML-powered products used by external customers.


• Exposure to both traditional machine learning and LLM-based applications.


• Strong communication and stakeholder management skills.


• Demonstrated ability to deliver measurable business results through machine learning solutions.