Specialist Software Engineer – Full Stack AI/ML/GenAI
Core
Designing, developing, and maintaining scalable software applications and MLOps pipelines that integrate generative AI and machine learning models into production environments for Amgen's biotechnology operations.
Role type
Senior Full Stack Software Engineer (AI/ML/GenAI)
Builds
Scalable software solutions, MLOps pipelines, and production ML models
Domain
Biotechnology / Generative AI / Machine Learning
Deliverable
production ML models | product features | infrastructure
Required skills
Full stack development, Python, JavaScript, SQL/NoSQL, machine learning algorithms, MLOps, cloud platforms, containerization, microservices, serverless architecture, rapid prototyping, code reviews, unit and integration testing, system monitoring, incident response
Preferred skills
TensorFlow, PyTorch, MLflow, Kubeflow, Airflow, AWS, GCP, Azure, Docker, Kubernetes, Prometheus, Grafana, Splunk, Hadoop, Spark
Technologies
Python, JavaScript, SQL, NoSQL, TensorFlow, PyTorch, MLflow, Kubeflow, Airflow, AWS, GCP, Azure, Docker, Kubernetes, Prometheus, Grafana, Splunk, Hadoop, Spark
Responsibilities
Take ownership of complex software projects from conception to deployment; Manage software delivery scope, risk, and timeline; Quickly translate concepts into working code using rapid prototyping; Provide technical guidance and mentorship to junior developers; Contribute to both front-end and back-end development using cloud technology; Develop innovative solutions using generative AI technologies; Conduct code reviews to ensure code quality and adherence to best practices; Create and maintain documentation on software architecture, design, deployment, disaster recovery, and operations; Develop and execute unit tests, integration tests, and other testing strategies to ensure the quality of the software; Identify and resolve software bugs and performance issues; Integrate systems and platforms to ensure seamless data flow and functionality; Collaborate with data scientists to develop, train, and evaluate machine learning models; Build and maintain MLOps pipelines leveraging cloud platform, including data ingestion, feature engineering, model training, deployment, and monitoring; Deploy machine learning models into production environments, ensuring that they are scalable and maintainable; Design and implement systems and processes to improve the reliability, scalability, and performance of applications; Automate routine operational tasks, such as deployments, monitoring, and incident response, to improve efficiency and reduce human error; Develop and maintain monitoring tools and dashboards to track system health, performance, and availability
Seniority
Senior, hands-on IC
