{"id":3229625,"url":"https://alion.io/job/maersk-associate-aiml-engineer-2","title":"Associate AI/ML Engineer","company":{"id":7034,"name":"Maersk","domain":"maersk.com","url":"https://alion.io/company/maersk","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":92,"open_postings":686,"ghost_share":0,"stale_share":0.504,"repost_share":0.006,"time_to_fill_p50_days":14,"computed_at":"2026-10-26T05:57:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Chengdu, China"],"countries":["CN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"Computer Vision","optional":false},{"name":"Hadoop","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"Python","optional":false},{"name":"Recommender Systems","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-10-22T00:00:00Z","employer_posted_date":"2026-10-22","last_verified_at":"2026-10-26T09:08:58Z","board_verified":true,"closed_at":null,"days_open":4,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":4},"description":"Data AI/ML (Artificial Intelligence and Machine Learning) Engineering involves the use of algorithms and statistical models to enable systems to analyze data, learn patterns, and make data-driven predictions or decisions without explicit human programming. AI/ML applications leverage vast amounts of data to identify insights, automate processes, and solve complex problems across a wide range of fields, including healthcare, finance, e-commerce, and more. AI/ML processes transform raw data into actionable intelligence, enabling automation, predictive analytics, and intelligent solutions. Data AI/ML combines advanced statistical modeling, computational power, and data engineering to build intelligent systems that can learn, adapt, and automate decisions.• Build and maintain machine learning models for various applications, such as natural language processing, computer vision, and recommendation systemsPerform exploratory data analysis (EDA) to identify patterns and trends in data\nClean, preprocess, perform hyperparameter tuning and analyze large datasets to prepare them for AI/ML model training\nBuild, test, and optimize machine learning models and experiment with algorithms and frameworks to improve model performance\nUse programming languages, machine learning frameworks and libraries, algorithms, data structures, statistics and databases to optimize and fine-tune machine learning models to ensure scalability and efficiency\nLearn to define user requirements and align solutions with business needs\nWork on AI/ML engineering projects, perform feature engineering and collaborate with teams to understand business problems\nLearn best practices in data / AI/ML engineering and performance optimization\nContribute to research papers and technical documentation\n• Contribute to project documentation and maintain data quality standards\nMaersk is committed to a diverse and inclusive workplace, and we embrace different styles of thinking. Maersk is an equal opportunities employer and welcomes applicants without regard to race, colour, gender, sex, age, religion, creed, national origin, ancestry, citizenship, marital status, sexual orientation, physical or mental disability, medical condition, pregnancy or parental leave, veteran status, gender identity, genetic information, or any other characteristic protected by applicable law.\nWe are happy to support your need for any adjustments during the application and hiring process. If you need special assistance or an accommodation to use our website, apply for a position, or to perform a job, please contact us by emailing .\nCORE SKILLSProgramming: Writing code to manipulate, analyze, and visualize data, often using languages like Python, R, and SQL.\nProficiency Level: Proficient\nAI & Machine Learning: Creating systems that can perform tasks that typically require human intelligence. Using Machine learning (ML), a subset of AI that uses algorithms to learn from and make predictions based on data\nProficiency Level: Foundational\nData Analysis: Inspecting, cleansing, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making\nProficiency Level: Foundational\nMachine Learning Pipelines: Using automated workflows that manage the end-to-end process of training and deploying machine learning models.\nProficiency Level: Proficient\nModel Deployment: Making a trained machine learning model available for use in production environments.\nProficiency Level: Foundational\nSPECIALIZED SKILLS\nBig Data Technologies: Using continuous integration and continuous delivery (CI/CD) pipelines to automate the process of software development, including building, testing, and deploying code\nNatural Language Processing (NLP): Focusing on the interaction between computers and humans through natural language.\nData Architecture: Designing and structuring of data systems, ensuring that data is stored, managed, and utilized efficiently\nData Processing Frameworks: Using tools and libraries to process large data sets efficiently, such as Apache Hadoop and Apache Spark.\nTechnical Documentation: Creating and maintaining documentation that explains the functionality, use, and maintenance of software or systems.\nDeep Learning: Using a subset of machine learning involving neural networks with many layers, used to model complex patterns in data.\nStatistical Analysis: Collecting and analyzing data to identify patterns and trends, and to make informed decisions.\nData Engineering: Designing and building systems for collecting, storing, and analyzing data at scale.\nDefinition of Proficiency Levels:\nFoundational: This is the entry level of the skill, typically expected when starting a new role or working with the skill for the first time. You rely on strong manager support, coaching, and training as you build the capability to progress to higher proficiency levels.\nProficient: This is the level at which you are considered effective in the skill. You demonstrate more than just functional competence-you begin to have a noticeable impact in your role by applying the skill consistently and meaningfully. You require only minimal support, coaching, or training to apply the skill successfully.\nAdvanced: This is the level where you move beyond meeting expectations to actively leading, influencing, and delivering considerable impact across the wider business. 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