United States onlySFI 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.
Primary Responsibilities:
- 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.
- 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.
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.
Additional Information
- In order to meet the clearance requirements for this opportunity, candidates must be authorized to work in the US
- All candidates will be subject to a complete background check to include, but not limited to Criminal History, Education Verification, Professional Certification Verification, Verification of Previous Employment and Credit History.
- Public Trust background investigations can take approximately four to eight weeks and requires fingerprinting.
Other Information
- The salary for this position is $100,000 - $170,000 annually
- For information on SFI's benefits please visit http://www.spatialfront.com/pages/career.html
- This is a full-time W2 position.
- Please no agencies, third parties, or Corp-to-corp.
- Spatial Front Inc. is an Equal-opportunity Employer, all qualified applicants will receive consideration for employment.
- Spatial Front Inc. participates in E-Verify.
CA and US only
