> Markdown version of [/jobs/ext/3070294-ai-ml-engineer-i](https://www.wearedevelopers.com/jobs/ext/3070294-ai-ml-engineer-i). 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). --- # AI/ML Engineer I - **Company:** Quzara Llc - **Location:** United States (Remote available) - **Experience:** Starter - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Audit Trail, Microsoft Azure, Software Bug Management, Continuous Integration, Data Cleansing, Software Debugging, Monitoring of Systems, Python (Programming Language), Machine Learning, Language Modeling, Performance Tuning, Search Technologies, Software Engineering, Systems Integration, Tokenization, Unstructured Data, Management of Software Versions, Data Logging, Cloud Platform System, Feature Engineering, Delivery Pipeline, Large Language Models, Prompt Engineering, Model Validation, Generative AI, Backend, Containerization, AI Platforms, Information Technology, HuggingFace, Machine Learning Operations, Restful APIs, GPT, Docker - **Published:** September 25, 2026 - **Apply:** https://www.dice.com/job-detail/68e9e4c7-9f0b-46c2-8315-697259140050 ## About the Role * Bachelor's degree in Computer Science, Engineering, Data Science, or related technical field * 1-3 years of professional experience in AI/ML engineering, data science, or software development * Strong proficiency in Python * Experience troubleshooting structured and unstructured data issues * Familiarity with data preprocessing, feature engineering, and model validation techniques * Hands-on experience working with OpenAI or similar LLM APIs * Familiarity with foundational models and smaller language models (SLMs) * Experience with transformer-based architecture (e.g., Hugging Face Transformers) * Understanding of embeddings, tokenization, fine-tuning concepts, and inference workflows * Experience in integrating AI services into cloud environments (Azure preferred) * Understanding of LLMOps concepts including model monitoring, logging, prompt iteration, and deployment workflows * Ability to debug model outputs and trace issues across data, prompts, and system integrations * Eligible for U.S. Public Trust clearance. Strongly Preferred Experience * Experience implementing RAG architecture and vector-based search systems * Familiarity with vector databases (e.g., FAISS, Pinecone, Azure AI Search) * Exposure to containerization (Docker) and CI/CD practices * Experience supporting AI systems in regulated or government environments * Understanding of model evaluation metrics and performance benchmarking ## Description * Develop, integrate, and maintain AI/ML components within secure, production-grade applications * Work directly with customers and internal engineering teams to troubleshoot system behavior and resolve defects * Perform bug fixes, root cause analysis, and performance optimization across AI-enabled services * Diagnosing and remediate data quality issues affecting model performance and system outputs * Support data preprocessing, transformation, validation, and feature engineering pipelines * Implement and optimize Large Language Model (LLM) integrations in secure cloud environments * Assist in prompt engineering, response evaluation, and model tuning * Support retrieval-augmented generation (RAG) pipelines and embedding workflows * Participate in LLMOps practices including monitoring, logging, versioning, and performance tracking of deployed models * Evaluate model outputs for consistency, hallucination risk, bias, and reliability * Integrate AI services via REST APIs within backend systems * Contribute to observability, telemetry, and audit logging aligned with compliance requirements * Document technical implementations in accordance with secure development standards Marginal Functions of the Job * Other duties as assigned Normal Work Schedule This full-time role runs Monday to Friday, 8:30 AM-5:30 PM and requires flexibility to work remotely or on-site (if applicable per client RTO policies). On occasion additional hours may be necessary. ## Related Videos - [Architecting the Future: Leveraging AI, Cloud, and Data for Business Success](https://www.wearedevelopers.com/videos/1096-architecting-the-future-leveraging-ai-cloud-and-data-for-business-success) - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Coffee with Developers - Maria Apazoglou](https://www.wearedevelopers.com/videos/1209-coffee-with-developers-maria-apazoglou) - [Speak, Code, Deploy: Transforming Developer Experience with Voice Commands](https://www.wearedevelopers.com/videos/1159-speak-code-deploy-transforming-developer-experience-with-voice-commands) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Got AI ideas but no money? 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