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# AI/ML Engineer

- **Company:** American IT Systems
- **Location:** Plano, TX, United States (Remote available)
- **Experience:** Expert
- **Salary:** $120,000.0 - $122,000.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Artificial Intelligence, Automated Storage and Retrieval Systems, Microsoft Azure, Cloud Computing, Databases, Continuous Integration, Python (Programming Language), PostgreSQL, Machine Learning, MongoDB, Open Source Technology, Standard Sql, Azure Machine Learning, Search Technologies, SQL Databases, Enterprise Data Management, Large Language Models, Multi-Agent Systems, Prompt Engineering, Apache Spark, Pyspark, Kubernetes, HuggingFace, Xgboost, Machine Learning Operations, Docker
- **Published:** August 29, 2026
- **Apply:** https://www.careerjet.com/jobad/us29464c66fee86bf446cdac342095888a

## About the Role

AI/ML Engineer with approx. 5 years of experience building production RAG platforms, multi-agent systems, and LLM retrieval pipelines using LangChain, LangGraph, GEMMA, and XGBoost across VR/XR * Languages, Databases: Python, SQL, PySpark, Spark, PostgreSQL, MongoDB

 * Gen AI & ML: LangChain, LangGraph, LlamaIndex, RAG, Claude 3.5 Sonnet, Mistral-7B, GPT-4o, Hugging Face (PEFT, TRL), LoRA, QLoRA, SFT, DPO, RAGAS, Prompt Engineering, GEMMA, UIR, DRAMA, Laser KNN, A/B Testing, NLP, Strong Python and SQL skills with solid machine learning and NLP fundamentals.
Experience with LangChain, LangGraph, LlamaIndex, prompt engineering, and retrieval optimization.
Familiarity with vector databases and search tools such as Pinecone, OpenSearch, FAISS, or pgvector.
Experience with Docker, Kubernetes, MLflow, CI/CD, and cloud platforms such as Azure.
Preferred Qualifications
Experience fine-tuning open-source models using LoRA, QLoRA, SFT, or DPO.
Background in high-scale, low-latency AI systems and enterprise data platforms.
Ability to work across research, engineering, and product teams to ship AI solutions into production.

## Description

Build and optimize LLM, RAG, and multi-agent workflows for production use cases.
Design retrieval systems using vector search, reranking, and metadata filtering.
Fine-tune, evaluate, and benchmark models to improve output quality and business impact.
Deploy AI/ML services with strong standards for latency, scalability, and observability.
Develop data, inference, and experimentation pipelines in partnership with product and engineering teams.


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