Key skills
Machine LearningPythonMLOpsTensorFlowAWSData PipelinesDeep LearningNLPData EngineeringBioinformaticsAIArtificial IntelligenceMLNatural Language ProcessingPyTorchscikit-learnMLflowKubeflowDatabricksSparkAirflowAzureGoogle CloudKubernetesDocker
About this role
Harrison Clarke is seeking a highly skilled AI / Machine Learning Engineer to support a large pharmaceutical client in the United States. The successful candidate will design, develop, and deploy machine learning and artificial intelligence solutions that accelerate drug discovery, optimize clinical trials, and improve healthcare data insights.
Responsibilities:
- Design, develop, and deploy scalable machine learning and AI models for pharmaceutical and healthcare data applications
- Build and maintain ML pipelines for data ingestion, feature engineering, model training, and deployment
- Work with large and complex datasets including clinical, genomic, and real-world evidence (RWE) data
- Implement deep learning, NLP, and predictive modeling techniques to support drug discovery and clinical research initiatives
- Collaborate with data scientists, research teams, and domain experts to translate business and scientific requirements into technical solutions
- Develop and maintain production-grade ML systems using modern MLOps practices
- Ensure models meet regulatory, privacy, and compliance requirements common in pharmaceutical environments
- Optimize model performance and monitor deployed models in production environments
- Contribute to documentation, technical standards, and best practices across AI/ML development
Requirements:
- Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, Bioinformatics, or related field
- 3+ years of experience developing machine learning solutions in production environments
- Strong programming skills in Python
- Experience with machine learning frameworks such as: TensorFlow, PyTorch, Scikit-learn
- Experience building data pipelines and ML workflows
- Experience with cloud platforms such as: AWS, Azure, Google Cloud
- Strong knowledge of data structures, algorithms, and software engineering principles
- Experience with MLOps tools such as Docker, Kubernetes, MLflow, or Kubeflow
- Experience in pharmaceutical, biotech, healthcare, or life sciences industries
- Knowledge of drug discovery, genomics, or clinical trial data
- Experience with Natural Language Processing (NLP) for clinical or medical data
- Familiarity with HIPAA, FDA, or GxP compliance requirements
- Experience working with large-scale biomedical datasets
- Experience with data engineering tools such as Spark, Airflow, or Databricks