Remote AI RAG Engineer — Production-Grade LLM Apps

Match Point Solutions; Maryland Heights (MO); Remote; USD 242,458,000 - 257,611,000; Full time

Job summary

Match Point Solutions is seeking two AI RAG Engineers to design, develop, and deploy production-grade Generative AI solutions, leveraging Retrieval-Augmented Generation and enterprise data sources. Candidates should be proficient in Python, LLMs, embeddings, vector databases, and REST APIs, with cloud deployment experience.

The roles involve building end-to-end AI pipelines, integrating with tools and enterprise systems, and ensuring security, observability, and scalable architectures across

Qualifications

• Bachelor's degree in CS/IT/Engineering or equivalent.
• 5+ years of software development experience.
• Hands-on experience building AI/ML or Generative AI applications.
• Strong Python and API development skills.

Responsibilities

• Design, develop, and deploy production-grade Generative AI and RAG applications.
• Build end-to-end AI solutions using LLMs, embeddings, vector databases, retrieval pipelines, and enterprise data sources.
• Develop AI agents and agentic workflows that interact with tools, APIs, databases, and enterprise applications.
• Create REST APIs and microservices for AI applications.
• Deploy AI solutions on AWS, Azure, or Google Cloud with containerization.
• Implement guardrails, observability, and production-grade testing.

Skills

Python development

LLMs

Vector databases

Embeddings

Prompt engineering

REST APIs

Education

Bachelor's degree in Computer Science, Information Technology, Engineering, or a related discipline

Tools

Pinecone

OpenSearch

Elasticsearch

Weaviate

Milvus

Job description

Match Point Solutions is seeking two AI RAG Engineers to design, develop, and deploy production-grade Generative AI solutions, leveraging Retrieval-Augmented Generation and enterprise data sources. Candidates should be proficient in Python, LLMs, embeddings, vector databases, and REST APIs, with cloud deployment experience.

The roles involve building end-to-end AI pipelines, integrating with tools and enterprise systems, and ensuring security, observability, and scalable architectures across