AI Engineer (ML/AI) with AWS (Onsite- DeerField IL) || Locals Only
About The Position
The AI Developer works with technical and business teams to develop generative AI solutions, including text extraction, speech-to-text, chat assistants with knowledge bases, chat assistants using transactional data, and AI-agent workflow automation. The role applies knowledge of generative AI frameworks and deployment architectures to build solutions and recommend best practices. The developer evaluates options, creates proofs of concept, and reviews code and solutions from other team members. Familiarity with full-stack architecture is a strong plus. The position is onsite in Deerfield, Illinois, and is listed for a 12-month duration.
Requirements
- At least 5 years of experience in design, implementation, or consulting in applications and infrastructures.
- Strong familiarity with AWS cloud services.
- At least 3 years of experience in various phases of machine learning, artificial intelligence, and deep learning solutions using AWS technologies.
- Experience implementing AI and generative AI solutions using AWS SageMaker, AWS Bedrock, and other AI services such as AWS Transcribe, AWS Textract, AWS Kendra, AWS Translate, and AWS Rekognition.
- Demonstrated understanding of prompt engineering and its application to the stated use cases.
- Experience evaluating LLM outputs for hallucination, toxicity, bias, and factual correctness.
- Experience implementing context caching and memory management patterns.
- Coding experience implementing solutions using various AWS services.
- Proficiency in creating and using REST APIs.
Nice To Haves
- Understanding of full-stack architecture.
- Proficiency in Python and NodeJS.
Responsibilities
- Work with technical and business teams to develop generative AI solutions for text extraction, speech-to-text, knowledge-base chat assistants, transactional-data chat assistants, and workflow automation using AI agents.
- Apply knowledge of generative AI frameworks and deployment architectures to build solutions and recommend best practices.
- Evaluate options and develop proofs of concept.
- Review code and solutions from other team members.
- Evaluate LLM outputs for hallucination, toxicity, bias, and factual correctness.
- Implement context caching and memory management patterns for well-performing applications.
- Implement solutions using AWS services.
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What This Job Offers
Career Level
Mid Level
Education Level
No Education Listed
