AI/ML Engineer

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.

Shape the Future with Generative AI

Are you passionate about harnessing cutting-edge Generative AI to create transformative real-world applications? Do you dream of building autonomous systems that learn, adapt, and make decisions independently? At Maersk, we’re reimagining how businesses solve complex problems with the power of Generative AI and Autonomous Agents.

We’re looking for a AI/ML Engineer to join our dynamic team and play a pivotal role in building AI solutions that will define the future of intelligent systems. If you're excited about applying your expertise to projects that disrupt industries and drive measurable impact, we want to hear from you! This is an exciting opportunity for self-motivated engineers with technical expertise, creative problem-solving skills, and a talent for disruptive process transformation using Gen AI

What you’ll do:

• Lead with autonomy: Take ownership of Gen AI projects from ideation to deployment, pushing boundaries of innovation

• Design the future: Develop and fine-tune Generative AI models (LLMs, diffusion models, GANs, VAEs, etc.) to optimize SCP business processes and enhance productivity;

• Empower AI agents: Create agent AI architectures for autonomous decision-making, task delegation, and multi-agent collaboration using Agentic AI frameworks like AutoGPT.

• Innovate with LLM’s: Build & optimize LLM’s applications, leveraging RAG and build robust Machine Learning pipelines for NLP, Multimodal AI tasks;

• Work with cutting-edge tools: Harness the power of Vector Databases (e.g., Pinecone, FAISS, ChromaDB) and LLM APIs (OpenAI, Anthropic, Hugging Face, Mistral, Llama).

• Collaborate for impact: Partner with cross-functional teams to integrate AI solutions into real-world applications like chatbots, copilots, automation tools, etc.).

• Stay ahead: Perform continuous research on state-of-the-art AI methodologies, exploring advancements in Generative AI, Autonomous Agents, and NLP to drive innovation.

Required Skills & Qualifications [Must have]

Strong foundation in Machine Learning & Deep Learning with expertise in neural networks, optimization techniques and model evaluation Experience with LLMs, Transformer architectures (BERT, GPT, LLaMA, Mistral, Claude, Gemini, etc.). Proficiency in Python, LangChain, Hugging Face transformers, MLOps. Experience with Reinforcement Learning and multi-agent systems for decision-making in dynamic environments. Knowledge of multimodal AI (integrating text, image, other data modalities into unified models.

Nice-to-have skills

Experience with Prompt Engineering, Fine-tuning, and RAG techniques. Familiarity with Cloud Platforms (AWS, GCP, Azure) and deployment tools like Docker, Kubernetes, FastAPI, or Flask.

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.

We 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 [email protected].

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

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.