{"id":1494244,"url":"https://alion.io/job/maersk-associate-aiml-engineer","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":94,"open_postings":1053,"ghost_share":0,"stale_share":0.431,"repost_share":0.004,"time_to_fill_p50_days":13,"computed_at":"2026-10-28T05:59:10Z"}},"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":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Azure","optional":false},{"name":"ComfyUI","optional":false},{"name":"Dify","optional":false},{"name":"Flowise","optional":false},{"name":"Langflow","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"OpenAI","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"SQL","optional":false},{"name":"CI/CD","optional":true},{"name":"Claude","optional":true},{"name":"Embeddings","optional":true},{"name":"Gemini","optional":true},{"name":"Hadoop","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-09-24T00:00:00Z","employer_posted_date":"2026-09-24","last_verified_at":"2026-10-28T08:40:23Z","board_verified":true,"closed_at":null,"days_open":34,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":34},"description":"Key Responsibilities\nCollaborate with business stakeholders, SMEs, and technology teams to understand business processes and identify AI automation opportunities.\nDesign, develop, and deploy AI-powered solutions using Large Language Models (LLMs), Generative AI, NLP, and workflow orchestration platforms.\nBuild and maintain AI workflows, copilots, and agent-based applications using platforms such as Dify, Coze, and similar low-code/no-code AI orchestration tools.\nIntegrate AI solutions with enterprise systems through APIs, databases, and cloud services.\nPerform data analysis, prompt engineering, experimentation, and evaluation to continuously improve solution quality and business impact.\nTranslate ambiguous business requirements into practical AI solutions that deliver measurable value.\nStay current with emerging AI technologies and promote best practices across the team.\nContinuously balance innovation, maintainability, scalability, and operational excellence.\nWe Are Looking For\n2-5 years of experience in AI, Data Science, Machine Learning, Software Engineering, or related fields.\nStrong proficiency in Python and SQL.\nHands-on experience with Generative AI technologies, including prompt engineering, Retrieval Augmented Generation (RAG), AI agents, and LLM-based applications.\nExperience with AI workflow platforms such as Dify, Coze, ComfyUI, LangFlow, Flowise, or similar tools.\nFamiliarity with API integration, JSON, REST services, and enterprise application connectivity.\nWorking knowledge of Azure cloud services, including Azure OpenAI, Function Apps, Web Apps, Storage, and related services.\nStrong problem-solving skills and a passion for building practical AI solutions that solve real business challenges.\nExcellent communication skills with the ability to collaborate effectively with both technical and non-technical stakeholders.\nBachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related field.\nNice to Have\nExperience developing AI agents, copilots, or workflow automation solutions in enterprise environments.\nExperience with vector databases, embeddings, RAG architectures, and AI evaluation frameworks.\nKnowledge of shipping, logistics, supply chain, customer service, or operations domains.\nExperience with Azure AI Foundry, OpenAI, Claude, Gemini, or similar AI platforms.\nExperience building production-grade applications and deploying solutions through CI/CD pipelines.\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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