AI/ML Engineer (AWS)

Reply•Los Angeles, CA

About The Position

As a Senior Developer, you will play a key role in building and evolving ML/AI applications and services on AWS. You will work closely with product, architecture, and engineering teams to deliver reliable, high-quality solutions that meet business and customer needs. You should be comfortable contributing across the software development lifecycle, from design and implementation through deployment, support, and continuous improvement.

Requirements

Must be US Citizen or green card holder
Bachelor’s degree in Computer Science, Engineering, or related field.
3 years experience delivering machine learning or AI-based solutions in production settings.
3 years experience programming in Python or a comparable language.
3 years experience working with large language models or Generative AI technologies.
3 years experience building and deploying solutions in AWS environments.
3 years experience establishing automated build, test, and release workflows for ML or data-driven applications.
3 years experience supporting deployed models, including monitoring performance and managing updates.

Nice To Haves

Master’s degree or higher in Computer Science, Engineering, or related field.
Experience with retrieval-augmented generation (RAG), embeddings, or agent-based architectures.
Experience designing distributed systems or working within cloud-based environments.
Experience integrating AI capabilities into customer-facing or enterprise applications.
Experience working in Agile development environments.
AWS certification

Responsibilities

Build and deliver scalable systems that incorporate machine learning and Generative AI capabilities.
Create, evaluate, and deploy machine learning solutions, including applications powered by large language models.
Establish and maintain datasets, testing strategies, and validation approaches to ensure model accuracy and stability.
Partner with product and engineering teams to translate business needs into practical, deployable AI solutions.
Integrate machine learning components into existing platforms and services.
Develop and manage automated pipelines that support continuous integration and delivery of machine learning solutions.
Monitor performance in production environments and refine systems based on real-world usage.