Design machine learning models to solve complex problems

Collaborate with interdisciplinary teams to understand business needs and identify machine learning opportunities

Deploy robust infrastructures for machine learning in production environments

Analyze data to validate model accuracy and reliability

Improve the performance and capabilities of existing machine learning models

Guide junior engineers and drive machine learning projects

Stay informed about the latest machine learning trends and advancements

Evaluate and integrate new data sources to expand model functionality

Document machine learning workflows and model architectures comprehensively

Present technical insights and model results to stakeholders and colleagues

Minimum of 3 years of experience in Machine Learning Engineering, emphasizing AWS technologies

Competency in Amazon Bedrock for creating and deploying machine learning solutions

Proficiency in Amazon SageMaker for building and maintaining machine learning workflows

Understanding of Amazon S3 for optimized data storage and processing in ML projects

Expertise in Python for the development of machine learning models

Background in Natural Language Processing (NLP) and real-world applications

Knowledge of Deep Learning strategies and their practical use

Skills in Prompt Engineering to refine model interactions

Capability to implement Sentiment Analysis in operational use cases

Familiarity with Model Evaluation practices for NLP in performance validation

Comfortable using English at a B2 level or higher for effective collaboration and communication

Background in alternative cloud solutions, including Google Cloud or Microsoft Azure

Proficiency in supplementary programming languages such as R or Java

Qualifications in machine learning, data science, or relevant technical fields