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# AI Engineer x5

- **Company:** Aventis Solutions
- **Location:** Miami, FL, United States
- **Salary:** $120,000.0 - $250,000.0
- **Contract:** Permanent contract
- **Skills:** AI Evaluation, JavaScript (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Cloud Engineering, Continuous Integration, Data Security, Data Structures, Python (Programming Language), Machine Learning, OpenAI, Software Deployment, Software Engineering, TypeScript, Enterprise Data Management, Google Cloud, Cloud Platform System, Feature Engineering, ReactJS, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Model Validation, Generative AI, Agentic-AI, Kubernetes, Low Latency, Machine Learning Operations, Api Design, Google Gemini, Terraform, Software Version Control, Data Pipelines, Serverless Computing, Docker
- **Published:** October 27, 2026
- **Apply:** https://www.careerboard.com/us/en/find-jobs-in-United-States/-8E95E78A4520713196/

## About the Role

* AI Engineering: Commercial experience building and deploying AI or machine learning applications, ideally within a production environment. You should be comfortable taking an idea from prototype through to a reliable, scalable solution.
 * Generative AI & LLMs: Hands-on experience with LLM-based applications, including OpenAI, Azure OpenAI, Anthropic, Gemini, or similar technologies. Experience with RAG, embeddings, vector databases, prompt engineering, AI agents, or tool calling is highly desirable.
 * Python Development: Strong Python engineering skills with experience building APIs, services, data pipelines, AI applications, or machine learning systems.
 * Modern Software Engineering: Strong understanding of software engineering fundamentals, version control, testing, APIs, cloud architecture, CI/CD, and production deployment. Experience with TypeScript, JavaScript, React, or similar technologies is a bonus.
 * AI Application Architecture: Understanding of how to connect models to enterprise data, applications, APIs, and business workflows. You should understand that building effective AI products requires considerably more than simply calling an LLM API.
 * Data & Machine Learning: Solid understanding of data structures, feature engineering, model evaluation, machine learning concepts, and analytical workflows. Experience working closely with data scientists and data engineers is highly valuable.
 * Cloud & Infrastructure: Experience working with AWS, Azure, GCP, or equivalent cloud environments. Familiarity with Docker, Kubernetes, Terraform, CI/CD, or serverless architecture is advantageous.
 * AI Evaluation & Observability: Understanding of how to evaluate AI applications in production, including accuracy, latency, cost, hallucination, model performance, monitoring, and continuous improvement.
 * Security & Responsible AI: Awareness of data security, privacy, access controls, responsible AI, and the challenges associated with deploying AI into customer-facing and enterprise environments.
 * Product Mindset: You don't just think about whether something can be built. You think about whether it should be built, who will use it, what problem it solves, and how success will be measured.