Artificial Intelligence/Machine Learning Engineer 1 - (NLP, AWS, PyTorch)

Akina, Inc. is hiring an Artificial Intelligence/Machine Learning Engineer 1 to design, develop, evaluate, and deploy AI/ML solutions, particularly language models. The role includes model training and optimization, AWS-based deployment, production integration, and collaboration with data scientists, product teams, and engineers.

Responsibilities:

• Designs, creates, tests, and productizes AI/ML algorithms to solve business challenges and support customer requirements
• Works across model architecture, data pipeline interaction, and metrics application, interpretation, and presentation; requires familiarity with application development, infrastructure management, data engineering, and data governance
• Uses training, retraining, deploying, scheduling, monitoring, and iterative improvement to create scalable solutions, and may lead geographically diverse teams as a primary POC for AI-related matters
• Build, train, and fine-tune machine learning models, particularly language models
• Apply best practices in model training, tuning, and optimization
• Design and implement solutions for model performance improvement
• Conduct rigorous model evaluation, including performance analysis and benchmarking
• Perform error analysis, debugging, and model diagnostics to ensure quality and reliability
• Work with cloud-based AI platforms, especially AWS Sagemaker, to deploy and scale models
• Integrate machine learning models into production environments, ensuring seamless integration with other systems
• Contribute to the development and maintenance of the full stack for AI model-based applications (front-end and back-end)
• Collaborate with software engineers to build scalable and efficient deployment pipelines
• Work closely with data scientists, product teams, and engineers to translate business requirements into technical solutions
• Document workflows, model design processes, and technical specifications
• Select appropriate data sets
• Perform statistical analysis
• Run machine learning algorithms
• Use results to improve models
• Train and retrain systems when needed
• Run standard test and evaluation protocols
• Provide system integration oversight