Description:
SFI seeks an AWS Machine Learning Engineer to join our team in developing an AI-driven platform that enhances the accessibility and analysis of structured and unstructured data. This role involves implementing advanced search capabilities, machine learning models, and a knowledge graph to extract insights, identify patterns, and support data-driven decision-making.
This role is focused on machine learning model development, NLP, and AI-driven automation using AWS Bedrock, SageMaker Unified Studio, and Comprehend. The ideal candidate has 5+ years of experience in LLM deployment, ML model training, NLP, and RAG (Retrieval-Augmented Generation) techniques. The role involves developing scalable AI models for generative and predictive analytics, text processing, and intelligent automation, leveraging AWS-native AI/ML solutions.

DESIRED SKILLS & QUALIFICATIONS

• Experience with AWS Neptune for graph-based ML to improve knowledge retrieval in LLMs.
• Familiarity with AWS Elasticsearch for AI-driven search solutions.
• Knowledge of AWS Lambda, Glue, Step Functions, and other serverless computing services.
• Experience with business intelligence tools such as Tableau or Power BI.
• Understanding of SAFe or other Agile frameworks.

Certifications: Preferred but not required:

• AWS Machine Learning Specialty or equivalent AI/ML certifications.

Requirements:

• A Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related technical field.
• 5+ years of experience in machine learning, LLM deployment, NLP, or AI-driven automation.
• Hands-on experience with AWS Bedrock for LLM fine-tuning, RAG, and generative AI applications.
• Proficiency in AWS SageMaker Unified Studio for managing the full ML model lifecycle.
• Experience using AWS Comprehend for NLP model development.
• Proficient in Python and SQL, with strong knowledge of data preprocessing and ML model tuning.
• Experience implementing vector databases, embeddings, and search pipelines for RAG architectures.
• Ability to work in an Agile environment and adapt to rapid changes in project requirements

• Develop, train, and fine-tune LLMs and ML models using AWS Bedrock, SageMaker Unified Studio, and Comprehend.
• Design and implement Retrieval-Augmented Generation (RAG) pipelines to improve LLM responses.
• Use SageMaker Unified Studio to manage the end-to-end ML lifecycle, including data preparation, training, tuning, and deployment.
• Build, deploy, and optimize NLP models for text classification, sentiment analysis, and entity recognition.
• Implement automated ML training pipelines, leveraging MLOps best practices in AWS.
• Collaborate with data engineers and software developers to integrate AI models into cloud-based applications.
• Utilize AWS Neptune for knowledge graphs to enhance LLM retrieval efficiency.
• Monitor, validate, and retrain models to ensure high performance in production environments.
• Stay up to date with AWS AI/ML advancements and recommend emerging tools and techniques.

Requirements:

• A Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related technical field.
• 5+ years of experience in machine learning, LLM deployment, NLP, or AI-driven automation.
• Hands-on experience with AWS Bedrock for LLM fine-tuning, RAG, and generative AI applications.
• Proficiency in AWS SageMaker Unified Studio for managing the full ML model lifecycle.
• Experience using AWS Comprehend for NLP model development.
• Proficient in Python and SQL, with strong knowledge of data preprocessing and ML model tuning.
• Experience implementing vector databases, embeddings, and search pipelines for RAG architectures.
• Ability to work in an Agile environment and adapt to rapid changes in project requirements.