Principal AI Engineer Optimisation Intelligence and Agent Orchestration - INBLR02 - Bangalore - Milesstone Buildcon | A.P. Moller - Maersk Job

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. This person will lead through specialists in optimisation, modelling, heuristics, analytics, and experimentation. They will define where AI can materially improve search, structural recommendations, and solver-backed workflows, while also guiding the design of multi-agent systems for reasoning, orchestration, exception handling, and human-in-the-loop execution. 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 escalate 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 • Raise standards across modelling practice, technical quality, experimentation discipline, and architectural judgment • Communicate assumptions, trade-offs, and limitations clearly to senior stakeholders and decision makers What we are looking for We are looking for a senior technical leader with deep credibility across both AI and optimisation, and with the ability to lead through highly skilled specialists rather than doing all modelling work personally. The strongest profile will combine experience in agentic AI systems and human-in-the-loop workflows with strong grounding in optimisation methods such as heuristics, search, mathematical programming, decomposition, or hybrid AI plus OR approaches. This person should also be comfortable directing teams working on modelling, experimentation, performance improvement, and implementation into production environments. Required qualifications • Advanced degree in engineering, mathematics, computer science, operations research, or another quantitative field • Significant experience leading applied AI, decision intelligence, optimisation, or analytical platform work in complex operational domains • Strong understanding of optimisation and modelling principles and how algorithms support real planning problems • Experience improving planning, routing, scheduling, or network optimisation through heuristics, analytics, or AI-based enhancements • Strong Python skills and comfort working closely with software and data engineering teams • Proven ability to lead experimentation, validate model performance, and convert technical ideas into business-relevant capabilities • Strong communication skills with the ability to explain technical trade-offs to non-technical stakeholders Classification: Internal 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.

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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: 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 dataProficiency Level: Advanced Data Analysis: Inspecting, cleansing, transforming, and modeling data to discover useful information, draw conclusions, and support decision-makingProficiency 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.