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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Advisor III, Machine Learning Engineer - **Company:** Phillips 66 - **Location:** Houston, TX, United States - **Experience:** Experienced - **Salary:** $125,100.0 - $152,900.0 - **Contract:** Temporary contract - **Skills:** Java (Programming Language), Artificial Intelligence, Amazon Web Services, Data Analysis, Automation of Tests, Microsoft Azure, Big Data, C Sharp (Programming Language), Cloud Computing, Continuous Integration, Information Engineering, Data Visualization, Distributed Systems, Graph Database, Python (Programming Language), Machine Learning, Power BI, Tensorflow, Scala (Programming Language), Software Deployment, Software Engineering, SQL Databases, Management of Software Versions, Data Storage Technologies, Feature Engineering, Pytorch, LangChain, Large Language Models, Llamaindex, Generative AI, Semi-structured Data, Scikit Learn, Information Technology, Performance Monitor, Data Management, Machine Learning Operations, Stream Processing, Semantic Kernel, Data Pipelines, Databricks, Programming Languages - **Published:** October 1, 2026 - **Apply:** https://dejobs.org/x/x/28ED27507BD341E7B67BDEE13C03A7B3/job/ ## About the Role * Legally authorized to work in the job posting country. * Bachelor's degree or higher in Computer Science, Engineering, Mathematics, Statistics, Physical Sciences, or a related field. * Three or more years of relevant experience in data engineering, analytics, data science, software development, or machine learning, including the ability to work independently on complex problems., * Experience with cloud-based analytics and ML environments, including Microsoft Azure, AWS, Databricks, or comparable platforms. * Experience with advanced ML platform engineering practices such as experiment tracking, CI/CD, observability, or model platform engineering. * Knowledge of large language models and generative AI, including tools or frameworks such as LangChain, LlamaIndex, Semantic Kernel, vector databases, and knowledge graphs. * Experience with analytics and visualization tools such as Power BI or Databricks. * Strong communication and collaboration skills, including the ability to explain technical concepts to diverse audiences and work effectively with non-technical partners. * Experience implementing data quality and governance practices for structured and semi-structured data. * Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, or comparable technologies. * Proficiency in at least one modern programming language, such as Python, C#, Java, Scala, or R. * Working knowledge of MLOps practices and the model lifecycle from development through production operation., Candidates for regular U.S. positions must be a U.S. citizen or national, or an alien admitted as permanent resident, refugee, asylee or temporary resident under 8 U.S.C. 1160(a) or 1255(a)(1). Individuals with temporary visas such as E, F-1, H-1, H-2, L, B, J, or TN or who need sponsorship for work authorization now or in the future, are not eligible for hire. ## Description The Advisor III, Machine Learning Engineering (Data & MLOps) owns the full lifecycle of production-grade artificial intelligence and machine learning solutions-from strategy and design through development, deployment, operation, and continuous improvement-to address high-value business problems across Phillips 66. This role combines strong machine learning engineering capabilities with the data engineering foundation required to build reliable, scalable, and maintainable AI products. The position partners closely with business leaders, data professionals, software engineers, and other technology teams to translate complex requirements into practical solutions. The work may support refining, transportation, commercial, marketing, and other data-intensive business areas, with a focus on improving efficiency, reliability, decision-making, and innovation. What You'll Do * Own the end-to-end delivery of machine learning models, algorithms, and AI-enabled digital products, from solution design and development through validation, deployment, production operation, and continuous improvement. * Take accountability for end-to-end ML solution outcomes, including data preparation, feature engineering, model development, evaluation, deployment, monitoring, and continuous improvement. * Apply machine learning, predictive analytics, and statistical methods to identify patterns, generate insights, automate processes, and improve business performance. * Develop scalable data and ML pipelines for storing, extracting, transferring, loading, transforming, modeling, and serving data for production systems and machine learning applications. * Design and implement large-scale data processing and modeling solutions using modern cloud, analytics, and open-source data science technologies. * Implement AI/ML solutions across Microsoft Azure, AWS, Databricks, and other enterprise platforms as appropriate for the business need. * Apply MLOps practices to operationalize models and AI products, including reproducible development, automated testing, versioning, deployment, monitoring, retraining, and governance. * Lead cross-functional delivery of ML solutions and remain accountable for model development, validation, production deployment, performance monitoring, and lifecycle management. * Own the translation of business objectives and diverse stakeholder requirements into robust, sustainable, measurable, and supportable AI/ML solutions. * Design, build, and maintain reliable data pipelines and data models that support machine learning, advanced analytics, reporting, and business intelligence. * Support batch and real-time data processing while applying sound data management, data quality, governance, and data storage practices. * Optimize data pipelines, SQL queries, analytical code, model-serving workflows, and distributed computing solutions for performance, scalability, reliability, and cost efficiency. * Prepare clear technical explanations, insights, and recommendations for both technical and non-technical audiences. * Own the ongoing performance and improvement of engineering processes, platform capabilities, and operational practices to increase efficiency and reduce failures and operational risk.