AI/ML Engineer

Cynet Systems • Plano, TX

18h

About The Position

This role requires a strong background in Machine Learning and Artificial Intelligence, with a focus on practical application and coding. The engineer will be responsible for leveraging AI coding assistants to translate business requirements into functional code, managing cloud infrastructure, and implementing advanced AI concepts like Generative AI, RAG, and Agentic AI. Experience with AWS services and various LLMs is crucial. The position also involves developing and optimizing AI features, potentially in the auto lending/finance sector.

Requirements

• 3-6+ years in ML/AI
• Strong Python hands-on programming (4-5 years)
• Extensive knowledge and expertise of leveraging GitHub Copilot/Claude Code/Amazon Q Developer to build coding prompts & contexts to convert business stories into quality working code & generate PRs.
• 1 year of GenAI hands on experience including working with different LLMs & Chatbots.
• 4+ years of AWS cloud experience, including EKS, ECS, S3, Lambda, Kafka event streaming, Amazon Bedrock/Claude/OpenAI, Redis Cache, NoSQL/SQL DB development.
• Solid knowledge of Prompt engineering/context engineering, Long-term vs short-term memory, Token management, RAG & Vectorization, Cache management, different frameworks of Agentic AI.

Nice To Haves

• Hands on development of building MCPs & building autonomous agentic workflows.
• Agentic AI Development including Multi-agent systems for autonomous workflows.
• A/B testing of AI features including validation of agentic real time decisioning.
• Auto Lending/Auto-finance background & experience.
• Hands on RAG implementation including vectorDBs.

Responsibilities

• Leverage GitHub Copilot/Claude Code/Amazon Q Developer to build coding prompts and contexts to convert business stories into quality working code.
• Generate Pull Requests (PRs) for code changes.
• Gain hands-on experience with Generative AI, including working with different LLMs and Chatbots.
• Utilize AWS cloud services such as EKS, ECS, S3, Lambda, Kafka event streaming, Amazon Bedrock/Claude/OpenAI, Redis Cache, and NoSQL/SQL databases.
• Apply knowledge of Prompt Engineering/Context Engineering, Long-term vs Short-term memory, Token Management, RAG & Vectorization, Cache Management, and different frameworks of Agentic AI.
• Develop Machine Critical Processes (MCPs) and build autonomous agentic workflows.
• Develop Agentic AI, including multi-agent systems for autonomous workflows.
• Conduct A/B testing of AI features and validate agentic real-time decisioning.
• Implement RAG solutions, including vector databases.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed