> Markdown version of [/jobs/ext/2964851-rag-engineer](https://www.wearedevelopers.com/jobs/ext/2964851-rag-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# RAG Engineer

- **Company:** Artmac Soft LLC
- **Location:** Prosper, TX, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** 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, Azure Machine Learning, Large Language Models, Grafana, Prompt Engineering, Generative AI, Git, Kubernetes, Restful APIs, Docker, Microservices
- **Published:** September 17, 2026
- **Apply:** https://www.careerjet.com/jobad/us2144340ff8b4111f379425147bc47394

## About the Role

* 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.



## Related Videos

- [RAG's Not Dead, You're Just Using It Wrong! - Phil Nash](https://www.wearedevelopers.com/videos/1906-rag-s-not-dead-you-re-just-using-it-wrong-phil-nash)
- [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94)
- [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered)
- [5 steps for running a Kubernetes environment at scale](https://www.wearedevelopers.com/videos/88-5-steps-for-running-a-kubernetes-environment-at-scale)
- [Building Blocks of RAG: From Understanding to Implementation](https://www.wearedevelopers.com/videos/1249-building-blocks-of-rag-from-understanding-to-implementation)
- [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews)

## Related Articles

- [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)
- [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline)
- [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers)
- [Introducing Redis Agent Memory Server](https://www.wearedevelopers.com/magazine/699-introducing-redis-agent-memory-server)
- [ Dev Digest 213: Petrol Prices, Agentic Workflows, AI Skills and CODE100!](https://www.wearedevelopers.com/magazine/718-dev-digest-213-petrol-prices-agentic-workflows-ai-skills-and-code100)
- [Building AI Solutions with Rust and Docker](https://www.wearedevelopers.com/magazine/494-building-ai-solutions-with-rust-and-docker)