RAG Engineer

Artmac Soft LLC
Prosper, TX, United States
10 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
8 years minimum
Working hours
Regular working hours

Tech stack

Application Programming Interfaces (APIs) Artificial Intelligence Cloud Computing Encodings Continuous Integration Data Transformation Elasticsearch Information Retrieval Python (Programming Language) Machine Learning Metadata Open Source Technology
+10 more
Azure Machine Learning Large Language Models Grafana Prompt Engineering Generative AI Git Kubernetes Restful APIs Docker Microservices

Requirements

  • Strong proficiency in Python and experience developing production-grade AI/ML applications.
  • Hands-on experience building and optimizing RAG architectures and pipelines.
  • Experience with vector databases such as Pinecone, Weaviate, Milvus, Qdrant, or Elasticsearch/OpenSearch.
  • Experience with RAG frameworks such as LangChain or LlamaIndex.
  • Experience with RAG evaluation frameworks and observability tools.
  • Experience deploying AI applications using Docker, Kubernetes, and cloud platforms.
  • Experience working with enterprise-scale documents and knowledge bases is a plus.
  • Experience with document chunking, preprocessing, metadata, and context management.
  • Hands-on experience with vector databases and similarity search.
  • Experience with RAG evaluation, benchmarking, and quality measurement.
  • Understanding of LLMs, prompt engineering, context windows, and hallucination mitigation.
  • Strong knowledge of information retrieval concepts such as BM25, dense retrieval, similarity search, and relevance scoring.
  • Experience designing scalable and reliable AI/ML services and APIs.
  • Familiarity with embedding and reranking models from leading open-source or commercial model providers.
  • Knowledge of FAISS, ANN search, vector indexing, and retrieval optimization.
  • Familiarity with CI/CD, Git, REST APIs, and microservices architecture.
  • Strong understanding of embeddings and semantic representations
  • Design and implement scalable Retrieval-Augmented Generation (RAG) pipelines for enterprise AI applications.
  • Develop effective document ingestion, preprocessing, chunking, and metadata enrichment strategies.
  • Build and optimize embedding pipelines using appropriate embedding models for semantic retrieval.
  • Design retrieval strategies for structured and unstructured enterprise data.
  • Evaluate and benchmark RAG systems using relevant retrieval and generation quality metrics.
  • Develop evaluation frameworks and datasets to measure precision, recall, relevance, groundedness, and answer quality.

Qualification: Bachelor’s degree or equivalent combination of education and experience.

About the company

Artmac Soft is a technology consulting and service-oriented IT company that provides innovative technology solutions and services to customers.

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Good distractions

Talks and stories from around this role — technically off-topic, practically not.

1:50 min

Simplifying generative AI deployments using the RagStack opinionated framework

David Leconte David Leconte +1 · World Congress 2024

6:21 min

Investigating push inefficiencies with upstream Git experts

Jonathan Creamer · Coffee With Developers

2:07 min

Inspecting default bridge architectures and custom Docker networks

Oliver Seitz Oliver Seitz · World Congress 2025

10:40 min

Visualizing Prometheus open metrics using custom Grafana dashboards

Stijn Polfliet · LIVE

38 sec

Introducing OpenRAG for custom data pipelines

Phil Nash · Coffee With Developers

56 sec

Favorite git commands and the importance of patch commits

Eileen Uchitelle Eileen Uchitelle +1 · Coffee With Developers

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