Senior Machine Learning Engineer

Harnham; Tampa (FL); On-site; USD 150,000 - 200,000; Full time

Job summary

Harnham is seeking a Senior Machine Learning Engineer to expand and support enterprise AI capabilities in the healthcare tech space. The role combines ML engineering, MLOps, cloud infrastructure, and generative AI operations with a focus on operational excellence.

You will deploy, monitor, and scale production ML systems, collaborate with data science teams, and drive measurable business outcomes through forecasting, classification, and recommendations.

Qualifications

• Advanced SQL proficiency and practical experience with AWS cloud services.
• Hands-on experience deploying ML models to production and monitoring them.
• Experience with SageMaker, cloud-based LLMs, and feature engineering pipelines.
• Strong ML lifecycle ownership (MLOps) and production-grade software practices.
• Healthcare industry exposure including regulated environments and compliance awareness.
• Strong communication and stakeholder management.

Responsibilities

• Design, build, and maintain production-grade ML systems and ML platforms.
• Deploy, monitor, and scale ML models in live environments.
• Develop scalable training and inference workflows and feature pipelines.
• Improve reliability, performance, and operational efficiency of ML workloads.
• Collaborate with data science teams to productionise analytics solutions.
• Develop and maintain MLOps practices, governance, and observability frameworks.
• Support generative AI/LLM applications and optimize prompts and workflows.
• Contribute to healthcare analytics projects including forecasting and recommendations.

Skills

Advanced SQL

AWS

SageMaker

Cloud-based LLM

DBT

Prefect

Airflow

Model monitoring

MLOps

Feature engineering

Infrastructure mindset

Healthcare domain

Responsible AI

Tools

Airflow

Prefect

SageMaker

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.