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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # ML Engineer - **Company:** Catalyst - **Location:** Jacksonville, FL, United States - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Amazon S3, Cloud Computing, Computer Programming, Data Transformation, Monitoring of Systems, Python (Programming Language), PostgreSQL, Machine Learning, Redis, Tensorflow, Software Deployment, Systems Architecture, TypeScript, Unstructured Data, Scripting, Cloud Platform System, Feature Engineering, Data Ingestion, Pytorch, Large Language Models, Fastapi, Information Technology, Data Management, Machine Learning Operations, Data Pipelines - **Published:** August 5, 2026 - **Apply:** https://www.careerbuilder.com/job-details/ml-engineer-jacksonville-fl--702fae6c-6e44-4e1a-94f3-8079ef31509e ## About the Role * Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field. * 1-6 years of professional experience in ML engineering. * Strong programming skills in Python (TypeScript experience is a plus). * Hands-on experience with ML frameworks such as PyTorch or TensorFlow. * Familiarity with cloud environments and infrastructure (preferably AWS). * Strong understanding of data pipeline design, real-time inference, and model monitoring. * Excellent communication skills with the ability to engage directly with customers and stakeholders. Core Experience * Proven experience building and deploying ML models into production environments. * Demonstrated ability to own the full model lifecyclefrom data ingestion and model development to deployment and monitoring. * Experience with audio-focused ML projects or similar domains involving unstructured data. * Proficiency in building scalable data pipelines for model training and evaluation. * Familiarity with FastAPI, OpenAI APIs, Baseten, LiteLLM, LiveKit, PostgreSQL, Redis, and S3 is a plus. * Solid grasp of ML systems architecture, feature engineering, evaluation strategies, and deployment best practices. Skills: Amazon Simple Storage Service (S3), Amazon Web Services (AWS), Analysis Skills, Application Programming Interface (API), Artificial Intelligence (AI), Best Practices, Cloud Computing, Coaching, Communication Skills, Computer Programming, Computer Science, Continuous Improvement, Cross-Functional, Customer Experience, Customer Relations, Data Management, Data Modeling, Data Science, Embedded Systems, Field Sales, Funding, Machine Learning, Machine Tool, Needs Assessment, PostgreSQL, Problem Solving Skills, Production Control, Production Systems, Python Programming/Scripting Language, Redis, Sales Closing Skills, Startup, System Architecture, Unstructured Data, Voice Products ## Description * Design, build, and deploy production-grade ML systems with end-to-end ownership of the model lifecyclefrom conception to deployment and maintenance. * Architect and deliver AI-powered solutions enabling natural speech interaction and real-time audio understanding. * Develop and optimize ML models focused on audio data to extract business-critical insights from previously unstructured voice data. * Build agents capable of operating natively on real-world audio inputs. * Collaborate with cross-functional teams to shape the foundations of the AI stack, improve tooling, and drive innovation in LLM and audio ML applications. * Work directly with customers to identify needs, gather feedback, and deliver impactful real-world solutions. * Handle the entire AI lifecycle, including data acquisition, preprocessing, model training, deployment, inference, and monitoring in production environments. * Participate in continuous improvement of the ML infrastructure and processes for scalability and performance. ## Related Videos - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Reducing LLM Calls with Vector Search Patterns - Raphael De Lio (Redis)](https://www.wearedevelopers.com/videos/1714-reducing-llm-calls-with-vector-search-patterns-raphael-de-lio-redis) - [Intro to FastAPI](https://www.wearedevelopers.com/videos/462-intro-to-fastapi) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Developer Experience, Platform Engineering and AI powered Apps](https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps) - [Accelerating Authentication Architecture: Taking Passwordless to the Next Level](https://www.wearedevelopers.com/videos/733-accelerating-authentication-architecture-taking-passwordless-to-the-next-level) ## 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) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Why Upskilling And Reskilling is Important For Developers](https://www.wearedevelopers.com/magazine/428-why-upskilling-and-reskilling-is-important-for-developers) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)