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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI and Machine Learning Engineer I Graduate - **Company:** Hewlett-Packard Enterprise - **Location:** San Jose, CA, United States - **Experience:** Starter - **Salary:** $92,700.0 - $187,800.0 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Data Analysis, Artificial Neural Networks, Big Data, Cloud Computing, Databases, Data Cleansing, Data Visualization, Python (Programming Language), Machine Learning, Natural Language Processing, Software Tools, Tensorflow, SQL Databases, Tableau (Software), Unstructured Data, Workflow Management Systems, Reinforcement Learning, Pytorch, Deep Learning, Matplotlib, Build Management, Scikit Learn, Information Technology, Data Analytics, Data Management, Unsupervised Learning, Databricks - **Published:** September 20, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=3e6fd1c0af4716aa ## About the Role * Bachelor's degree in computer science, engineering, data science, machine learning, artificial intelligence, or closely related quantitative discipline. Master's degree is desirable. * Typically, 0-2 years' experience. Knowledge and Skills: * Proficiency in programming languages such as Python, R, or Java is required. Knowledge of libraries and frameworks commonly used in AI and machine learning, such as TensorFlow, PyTorch, or scikit-learn, is highly beneficial. * A solid understanding of statistics, probability, linear algebra, calculus, and optimization methods is crucial for building and evaluating machine learning models. * In-depth knowledge of machine learning algorithms, techniques, and concepts is essential. This includes supervised and unsupervised learning, deep learning, neural networks, reinforcement learning, and natural language processing. * Proficiency in working with large datasets, data pre-processing, data cleaning, and exploratory data analysis is necessary. Experience with SQL and databases and data visualization tools like Matplotlib or Tableau is required. ## Description Develops and programs integrated software algorithms to structure, analyze and leverage structured and unstructured data in product and systems applications. Can work with large scale computing frameworks, data analysis systems, and modeling environments. Uses machine learning and statistical modeling techniques to improve product/system performance, data management, quality, and accuracy. Formulates descriptive, diagnostic, predictive and prescriptive insights/algorithms and translates technical specifications into code. Applies, optimizes and scales deep learning technologies and algorithms to give computers the capability to visualize, learn and respond to complex situations. Documents procedures for installation and maintenance, completes programming, performs testing and debugging, defines and monitors performance metrics. Contributes to the success of HPE by translating customer requirements and industry trends into AI/ML products, solutions, and systems improvement projects. Management Level Definition: Contributes to assignments of limited scope by applying technical concepts and theoretical knowledge acquired through specialized training, education, or previous experience. Acts as team member by providing information, analysis and recommendations in support of team efforts. Exercises independent judgment within defined parameters., * Partner with cross-functional teams to identify opportunities for data-driven improvements and translate business needs into technical solutions. * Ingest and integrate data from structured and unstructured enterprise systems, including Databricks and IT data platforms. * Design and build curated data models and analytical layers to support reporting and downstream analytics. * Develop, optimize, and maintain pipelines using SQL, Python, and orchestration tools. * Clean, transform, and prepare datasets for reliable consumption across the business. * Ensure data quality and observability across pipelines, workflows, and storage layers. * Integrate new data sources, APIs, and event streams into the platform. * Create clear data documentation and communicate technical concepts to non-technical stakeholders. * Stay current with modern data engineering tools, cloud technologies, and best practices. ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [6 Reasons to Use Java For Your Next AI Project](https://www.wearedevelopers.com/magazine/111-6-reasons-to-use-java-for-your-next-ai-project) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [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)