AI/ML Engineer (Data Engineering + AI Focus)

Job Summary

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.

Responsibilities

• Lead the development of a global Data and AI platform - the backbone of data AI innovation across APM Terminals.
• Act as a hands-on technical leader - leading by example, owning architecture decisions, writing key parts of the codebase, and tackling complex problems alongside your team.
• Build for reuse and scale - designing shared data models, KPIs, pipelines, and APIs that empower teams across APMT to innovate faster.
• Infuse the platform with intelligence - applying AI to improve data quality, automate monitoring, and boost developer productivity.
• Advance our ML / AI Ops capabilities - ensuring seamless model training, deployment, monitoring, and lifecycle management for reliable AI in production.
• Empower teams to build and scale AI GenAI solutions - by providing trusted data and robust, production-ready infrastructure.
• Work with product, architecture, and business stakeholders - to turn real needs from operations, commercial, finance, and more into durable platform features.
• Coach and mentor a talented engineering team - inspiring a high bar for quality, ownership, and continuous improvement.

Requirements

• Deep engineering experience - strong background in data engineering, including designing and operating large-scale data and AI platforms.
• Proven leadership - you ve managed and developed engineers while staying technical (architecture, code reviews, even coding when needed).
• Modern architecture skills - solid understanding of cloud-native data architectures, platform engineering, and best practices in building robust, scalable data systems.
• AI/ML exposure - hands-on experience with AI/ML or data science systems in production, and familiarity with MLOps / AI Ops practices to support model deployment and monitoring.
• Automation quality mindset - experience with AI-assisted development or intelligent automation for data quality/observability.

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 data Proficiency Level: Proficient
• Data Analysis: Inspecting, cleansing, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making Proficiency 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: Proficient

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
• 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

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