AI Engineer: NLP, Prompting & Vector Data Systems

Synchrony Systems, Inc.

Minneapolis (MN)

On-site

USD 80,000 - 95,000

Full time

2 days ago
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Job summary

Synchrony Systems, Inc. in Minneapolis, MN is seeking a Python-focused candidate with 1.5 to 4 years of experience to join our AI/ML team. The role emphasizes building and evaluating intelligent agents, prompt engineering, and working with NLP or chatbots in a collaborative, sprint-driven environment.

You will work with embedding models, vector databases, and RESTful APIs, contribute to data handling, and ensure robust testing and bias awareness in model outputs.

Qualifications

  • Solid grasp of machine learning and AI concepts, NLP or chatbots.
  • Prompt engineering: crafting prompts, few-shot examples and output formatting.
  • Familiarity with embedding models, vector databases, and unstructured data.
  • Building and consuming RESTful APIs, microservices, and handling JSON/XML data.
  • Knowledge of lists, dictionaries, trees/graphs and their use in agent design.
  • Strong debugging and troubleshooting to differentiate model vs. code issues.
  • Agile and collaborative mindset; sprint-based teamwork.
  • Domain adaptability for new industries and tailored agents.
  • Attention to data handling, edge cases, and thorough testing.
  • Ethical awareness of bias and confidentiality considerations.

Skills

AI/ML concepts
Prompt engineering
RAG & data handling
API & integration
Data structures & algorithms
Debugging & problem-solving
Agile mindset
Domain adaptability
Attention to detail
Ethical awareness

Job description

Synchrony Systems, Inc. in Minneapolis, MN is seeking a Python-focused candidate with 1.5 to 4 years of experience to join our AI/ML team. The role emphasizes building and evaluating intelligent agents, prompt engineering, and working with NLP or chatbots in a collaborative, sprint-driven environment.

You will work with embedding models, vector databases, and RESTful APIs, contribute to data handling, and ensure robust testing and bias awareness in model outputs.

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