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
Job source
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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