Principal AI Engineer Optimisation Intelligence and Agent Orchestration

A.P. Moller - Maersk; Bengaluru; Presencial; INR 4 000 000 - 9 000 000; Tempo integral

Resumo da oferta

Maersk is seeking a Principal AI Engineer to lead AI-augmented optimisation and intelligent operational decisioning across global networks. You will shape the technical direction for AI-enabled planning, design, and execution, working with cross-functional teams to improve throughput, reliability, and efficiency.

The role requires senior leadership in AI/OR, strong background in optimisation methods, and experience deploying ML in production within logistics or supply chain contexts.

Qualificações

• Experience in logistics, supply chain, shipping, transportation, or another optimisation-heavy industry.
• Experience with AI or ML to improve optimisation performance, such as learning-augmented algorithms or surrogate models.
• Experience across research, prototyping, and productionisation of optimisation methods.
• Leadership experience in AI/OR architecture and cross-functional teams.

Responsabilidades

• Technical direction for AI-augmented optimisation and agentic decision systems across Network Design and operational execution.
• Define how planning problems are framed and where AI can improve search, candidate generation, and decision quality.
• Interface between AI systems, heuristics, solver layers, optimisation services, and enterprise workflows.
• Set patterns for when AI should recommend, act autonomously, or elevate to humans.
• Drive structured experimentation to compare methods, validate model behaviour, and improve performance.

Conhecimentos

Python

R

SQL

Data Analysis

Machine Learning

Deep Learning

NLP

Data Engineering

Model Deployment

Big Data

Ferramentas

Hadoop

Apache Spark

CI/CD

NLP Tooling

Descrição da oferta de emprego

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.

Principal AI Engineer AI, Optimisation Intelligence and Agent Orchestration

About the role We are building an AI-powered operating model for Ocean Operations and a next-generation optimisation capability for global Network Design. This role is not limited to agent orchestration or execution workflows. It is a senior technical leadership role for how AI strengthens optimisation methods, modelling, and decision quality across both planning and execution. The remit is broader than a standalone AI feature set. It covers how AI, optimisation, and engineering come together to deliver better network decisions at scale, while also shaping agentic decision systems in Control Tower style execution environments.

Role purpose

The Principal AI Engineer will set the technical direction for AI-augmented optimisation and intelligent operational decisioning.

What you will own

• Technical direction for AI-augmented optimisation and agentic decision systems across Network Design and operational execution


• How planning problems are framed and where AI can improve search, candidate generation, structural recommendations, and decision quality


• The interface between AI systems, heuristics, solver layers, optimisation services, and enterprise workflows


• Patterns for when AI should recommend, act autonomously, or elevate to humans


• The quality bar for experimentation, evaluation, and translation of new methods into measurable value for planners and decision-makers


• Reusable intelligence patterns that can scale across both Control Tower and Network Design use cases

Key responsibilities

• Lead the optimisation intelligence agenda across network design, routing, vessel deployment, cargo flow, and operational decisioning


• Set direction for how teams define problem structures, formulate optimisation approaches, design heuristics, and identify where AI can improve search, modelling quality, and decision outcomes

Classification: Internal

• Guide specialists building optimisation models, analytical components, and AI capabilities, ensuring their work fits into a coherent AI plus OR architecture


• Drive structured experimentation to compare alternative methods, validate model behaviour, and improve performance, robustness, and scalability


• Define how AI systems should interact with solver layers, heuristics, and optimisation services rather than treating AI as a separate layer


• Lead the use of agentic AI where it adds value to reasoning, orchestration, exception handling, and human-in-the-loop decision support


• Shape the innovation roadmap by identifying promising new methods and accelerating them into pilots and scaled capabilities

Preferred qualifications

• Experience in logistics, supply chain, shipping, transportation, or another optimisation-heavy industry


• Exposure to large-scale network design, routing, fleet planning, cargo flow optimisation, or similar decision systems


• Experience using AI or ML to improve optimisation performance, such as learning-augmented algorithms, surrogate models, search guidance, or reinforcement learning


• Experience working across research, prototyping, and productionisation of optimisation methods

Leadership expectations

This role is expected to lead through others. The Principal AI Engineer should create the technical direction, decision framework, and quality standards for a broader team of optimisation, analytics, and AI specialists. The role should not be framed as a lone architect writing models end to end. It should be framed as the senior leader who sets the optimisation intelligence agenda and ensures that specialist work compounds into a coherent capability.

CORE SKILLS

Programming: Writing code to manipulate, analyze, and visualize data, often using languages like Python, R, and SQL. Proficiency Level: Advanced 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: Advanced Data Analysis: Inspecting, cleansing, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making Proficiency Level: Proficient Machine Learning Pipelines: Using automated workflows that manage the end-to-end process of training and deploying machine learning models. Proficiency Level: Advanced Model Deployment: Making a trained machine learning model available for use in production environments. Proficiency Level: Advanced 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.

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. We will consider qualified applicants with criminal histories in a manner consistent with all legal requirements.

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