{"id":1494271,"url":"https://alion.io/job/maersk-principal-ai-engineer-optimisation-intelligence-and-agent-orchestration","title":"Principal AI Engineer Optimisation Intelligence and Agent Orchestration","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":95,"open_postings":682,"ghost_share":0,"stale_share":0.204,"repost_share":0.001,"time_to_fill_p50_days":37,"computed_at":"2026-10-31T05:58:55Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":23000,"max_usd":61000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":47},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Python","optional":false},{"name":"CI/CD","optional":true},{"name":"Hadoop","optional":true},{"name":"NLP","optional":true},{"name":"Reinforcement Learning","optional":true},{"name":"Spark","optional":true},{"name":"SQL","optional":true}],"status":"live","first_seen_at":"2026-09-24T00:00:00Z","employer_posted_date":"2026-09-24","last_verified_at":"2026-10-31T20:09:09Z","board_verified":true,"closed_at":null,"days_open":37,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":37},"description":"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\nTechnical direction for AI-augmented optimisation and agentic decision systems across Network Design and operational execution\nHow planning problems are framed and where AI can improve search, candidate generation, structural recommendations, and decision quality\nThe interface between AI systems, heuristics, solver layers, optimisation services, and enterprise workflows\nPatterns for when AI should recommend, act autonomously, or escalate to humans\nThe quality bar for experimentation, evaluation, and translation of new methods into measurable value for planners and decision-makers\nReusable intelligence patterns that can scale across both Control Tower and Network Design use cases Key responsibilities\nLead the optimisation intelligence agenda across network design, routing, vessel deployment, cargo flow, and operational decisioning\nSet 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\nGuide specialists building optimisation models, analytical components, and AI capabilities, ensuring their work fits into a coherent AI plus OR architecture\nDrive structured experimentation to compare alternative methods, validate model behaviour, and improve performance, robustness, and scalability\nDefine how AI systems should interact with solver layers, heuristics, and optimisation services rather than treating AI as a separate layer\nLead the use of agentic AI where it adds value to reasoning, orchestration, exception handling, and human-in-the-loop decision support\nShape the innovation roadmap by identifying promising new methods and accelerating them into pilots and scaled capabilities\nRaise standards across modelling practice, technical quality, experimentation discipline, and architectural judgment\nCommunicate 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\nAdvanced degree in engineering, mathematics, computer science, operations research, or another quantitative field\nSignificant experience leading applied AI, decision intelligence, optimisation, or analytical platform work in complex operational domains\nStrong understanding of optimisation and modelling principles and how algorithms support real planning problems\nExperience improving planning, routing, scheduling, or network optimisation through heuristics, analytics, or AI-based enhancements\nStrong Python skills and comfort working closely with software and data engineering teams\nProven ability to lead experimentation, validate model performance, and convert technical ideas into business-relevant capabilities\nStrong communication skills with the ability to explain technical trade-offs to non-technical stakeholders Classification: Internal Preferred qualifications\nExperience in logistics, supply chain, shipping, transportation, or another optimisation-heavy industry\nExposure to large-scale network design, routing, fleet planning, cargo flow optimisation, or similar decision systems\nExperience using AI or ML to improve optimisation performance, such as learning-augmented algorithms, surrogate models, search guidance, or reinforcement learning\nExperience 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.\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. We will consider qualified applicants with criminal histories in a manner consistent with all legal requirements.\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: Advanced\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: Advanced\nData Analysis: Inspecting, cleansing, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making\nProficiency Level: Proficient\nMachine Learning Pipelines: Using automated workflows that manage the end-to-end process of training and deploying machine learning models.\nProficiency Level: Advanced\nModel Deployment: Making a trained machine learning model available for use in production environments.\nProficiency Level: Advanced\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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