Associate AI/ML Engineer - CNCGD02 - Chengdu - 1-4 Floor | A.P. Moller - Maersk Job

Key Responsibilities

• Collaborate with business stakeholders, SMEs, and technology teams to understand business processes and identify AI automation opportunities.
• Design, develop, and deploy AI-powered solutions using Large Language Models (LLMs), Generative AI, NLP, and workflow orchestration platforms.
• Build and maintain AI workflows, copilots, and agent-based applications using platforms such as Dify, Coze, and similar low-code/no-code AI orchestration tools.
• Integrate AI solutions with enterprise systems through APIs, databases, and cloud services.
• Perform data analysis, prompt engineering, experimentation, and evaluation to continuously improve solution quality and business impact.
• Translate ambiguous business requirements into practical AI solutions that deliver measurable value.
• Stay current with emerging AI technologies and promote best practices across the team.
• Continuously balance innovation, maintainability, scalability, and operational excellence.

We Are Looking For

• 2-5 years of experience in AI, Data Science, Machine Learning, Software Engineering, or related fields.
• Strong proficiency in Python and SQL.
• Hands-on experience with Generative AI technologies, including prompt engineering, Retrieval Augmented Generation (RAG), AI agents, and LLM-based applications.
• Experience with AI workflow platforms such as Dify, Coze, ComfyUI, LangFlow, Flowise, or similar tools.
• Familiarity with API integration, JSON, REST services, and enterprise application connectivity.
• Working knowledge of Azure cloud services, including Azure OpenAI, Function Apps, Web Apps, Storage, and related services.
• Strong problem-solving skills and a passion for building practical AI solutions that solve real business challenges.
• Excellent communication skills with the ability to collaborate effectively with both technical and non-technical stakeholders.
• Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related field.

Nice to Have

• Experience developing AI agents, copilots, or workflow automation solutions in enterprise environments.
• Experience with vector databases, embeddings, RAG architectures, and AI evaluation frameworks.
• Knowledge of shipping, logistics, supply chain, customer service, or operations domains.
• Experience with Azure AI Foundry, OpenAI, Claude, Gemini, or similar AI platforms.
• Experience building production-grade applications and deploying solutions through CI/CD pipelines.

Maersk 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.
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CORE SKILLS Programming: Writing code to manipulate, analyze, and visualize data, often using languages like Python, R, and SQL.Proficiency Level: Proficient AI & 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 dataProficiency Level: Foundational Data Analysis: Inspecting, cleansing, transforming, and modeling data to discover useful information, draw conclusions, and support decision-makingProficiency Level: Foundational Machine Learning Pipelines: Using automated workflows that manage the end-to-end process of training and deploying machine learning models.Proficiency Level: Proficient Model Deployment: Making a trained machine learning model available for use in production environments.Proficiency Level: Foundational SPECIALIZED SKILLS Big Data Technologies: Using continuous integration and continuous delivery (CI/CD) pipelines to automate the process of software development, including building, testing, and deploying code Natural Language Processing (NLP): Focusing on the interaction between computers and humans through natural language. Data Architecture: Designing and structuring of data systems, ensuring that data is stored, managed, and utilized efficiently Data Processing Frameworks: Using tools and libraries to process large data sets efficiently, such as Apache Hadoop and Apache Spark. Technical Documentation: Creating and maintaining documentation that explains the functionality, use, and maintenance of software or systems. Deep Learning: Using a subset of machine learning involving neural networks with many layers, used to model complex patterns in data. Statistical Analysis: Collecting and analyzing data to identify patterns and trends, and to make informed decisions. Data Engineering: Designing and building systems for collecting, storing, and analyzing data at scale. Definition of Proficiency Levels: Foundational: 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. Proficient: 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. Advanced: This is the level where you move beyond meeting expectations to actively leading, influencing, and delivering considerable impact across the wider business. You are seen as a role model, demonstrate the skill independently, and require little to no manager support.