> Markdown version of [/jobs/ext/2388808-specialist-data-sciences](https://www.wearedevelopers.com/jobs/ext/2388808-specialist-data-sciences). 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). --- # Specialist - Data Sciences - **Company:** LTM Inc - **Location:** Irving, TX, United States - **Salary:** $83,912.0 - $128,080.0 - **Contract:** Internship / Graduate position - **Skills:** Clean Code Principles, A/B Testing, Artificial Intelligence, Amazon Web Services, Artificial Neural Networks, Microsoft Azure, Information Engineering, Software Debugging, Python (Programming Language), Machine Learning, Open Source Technology, Performance Tuning, Tensorflow, Software Engineering, Management of Software Versions, Google Cloud, Feature Engineering, Pytorch, Large Language Models, Prompt Engineering, Deep Learning, Generative AI, ONNX (Open Neural Network Exchange) Format, HuggingFace, Machine Learning Operations, Docker - **Published:** August 8, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=22c33033eaab5731 ## About the Role * Mentor junior engineers and lead technical reviews for ML code pipelines and deployment implementations 712 years of experience in software engineering data engineering ML engineering with significant hands on time delivering ML solutions * Strong proficiency in Python and MLDL libraries such as PyTorch TensorFlow and familiarity with modern model ecosystems eg Hugging Face * Solid understanding of ML fundamentals feature engineering model selection evaluation metrics overfitting cross validation and deep learning concepts neural nets transformers where relevant * Experience with model deployment approaches tools eg model serving ONNX Torch Serve Triton or equivalent * Strong engineering practices clean code debugging performance optimization API integration and collaboration in cross functional teams * Experience with MLOps GenAIOps tooling such as MLflow containerization Docker and cloud platforms AWS, Azure, GCP for scalable ML delivery Experience with LLMs Generative AI fine tuning prompt engineering evaluation and production patterns * Familiarity with RAG and vector databases plus responsible ethical AI practices and governance * Experience building automated benchmarking AB testing and monitoring frameworks for ML systems * Contributions to open source publications patents or strong internal innovation track record Strong ownership and ability to lead quality outcomes end-to-end * Clear communication and stakeholder management, Mandatory Skills : GenAI - LLMOps, Machine Learning - AIOPS, MLOPS, Python - Data Science ## Description * Design develop and deploy MLAI models for real time and batch use cases including model experimentation training and evaluation * Build and optimize inference pipelines and integrate ML capabilities into applications services in partnership with product and engineering teams * Develop and maintain data pipelines for model training validation and continuous improvement retraining continual learning * Monitor model performance in production quality drift bias hallucinations where applicable and drive improvements in reliability and robustness * Establish engineering best practices for ML delivery reproducibility versioning testing documentation and benchmarking experimentation * Contribute to solution architecture decisions for ML systems data compute deployment patterns and operational controls ## Related Videos - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [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) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Getting Started with Machine Learning](https://www.wearedevelopers.com/videos/260-getting-started-with-machine-learning) - [DevOps for Machine Learning](https://www.wearedevelopers.com/videos/179-devops-for-machine-learning) ## Related Articles - [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) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)