Machine Learning Developer (WOS284)

Machine Learning Developer (WOS284)

.Job Title: Machine Learning DeveloperJob Location: Mexicali, MexicoOur Division: Trimble ForestryAbout the Role:As a Machine Learning Engineer at Trimble Forestry, you will play a pivotal role in developing and programming integrated software algorithms to structure, analyze, and leverage data in product and systems applications in both structured and unstructured environments.
You will be at the forefront of developing and communicating descriptive, diagnostic, predictive,



and prescriptive insights/algorithms to drive innovation and efficiency in our products and systems.Join Trimble Forestry and contribute to our mission of improving the productivity and sustainability of the global forest supply chain through innovative and data-driven solutions.
As a Machine Learning Engineer, you will have the opportunity to work on cutting-edge projects that leverage the latest in machine learning and artificial intelligence technologies to make a real-world impact.What You Will DoAnomaly Detection & Data Classification: Develop, implement, and optimize AI/ML models for detecting anomalies and classifying large datasets across multiple domains.LLM Evaluation & Selection: Evaluate, select, and fine-tune large language models (LLMs) for various use cases, ensuring high performance, reliability, and alignment with product requirements.Scripting & Programming: Write clean, efficient code in scripting (e.G., Python) and object-oriented languages (e.G., C++) to support AI/ML development and deployment.Data Warehousing & SQL Expertise: Design and manage scalable data solutions using SQL and data warehouses such as Snowflake, Databricks, or Microsoft Synapse.
Ensure that data pipelines support high-volume data processing and analysis needs.Natural Language Processing/Understanding (NLP/NLU):



Apply NLP/NLU technologies to extract insights from unstructured data and enhance machine learning models for language-based tasks.AI/ML Infrastructure: Build scalable, reusable AI infrastructure that can support multiple teams and projects, ensuring consistent performance and reliability.ML Ops & Deployment: Establish and maintain robust AI/ML operations (ML Ops) practices, ensuring smooth, efficient deployment, and continuous monitoring of AI systems in production environments.Prompt Engineering & Best Practices: Develop and promote best practices in prompt engineering, ensuring that AI solutions are both innovative and aligned with the latest advancements in AI technology.What Skills & Experience You Should Have:Bachelor's or Master's degree in Computer Science, Data Science,



or a related field.Proven experience in anomaly detection and data classification using AI/ML models.Strong knowledge of scripting (Python) and object-oriented languages (C++).Proficiency in SQL and experience with data warehouses such as Snowflake, Databricks, or Microsoft Fabric.Solid understanding of NLP/NLU technologies

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