Maersk Global Service - AI/ML Engineer

Job Description :

- Design, develop, and implement robust, scalable, and optimized machine learning and deep learning models, with the ability to iterate with speed

- Write and integrate automated tests alongside models or code to ensure reproducibility, scalability, and alignment with established quality standards

- Implement best practices in security, pipeline automation, and error handling using programming and data manipulation tools

- Identify and implement the right data-driven approaches to solve ambiguous and open-ended business problems, leveraging data engineering capabilities

- Research and implement new models, technologies, and methodologies and integrate these into production systems, ensuring scalability and reliability

- Apply creative problem-solving techniques to design innovative tools, develop algorithms and optimized workflows

- Independently manage and optimize data solutions, perform A/B testing, evaluate performance and evaluate performance of systems

- Understand technical tools and frameworks used by the team, including programming languages, libraries, and platforms and actively support debugging or refining code in projects

- Contribute to the design and documentation of AI/ML solutions, clearly detailing methodologies, assumptions, and findings for future reference and cross-team collaboration

- Collaborate across teams to develop and implement high-quality, scalable AI/ML solutions that align with business goals, address user needs, and improve performance

Foundational Skills :

- Have mastered the concepts and can demonstrate Programming skills in complex scenarios.

- Understands the below skills beyond the fundamentals and can demonstrate in most situations without guidance

1. AI & Machine Learning

2. Data Analysis

3. Machine Learning Pipelines

4. Model Deployment and Tuning

Specialized Skills :

- To be able to understand beyond the fundamentals and can demonstrate in most situations without guidance for the following skills :

1. Simulation and Optimization Techniques

2. Statistical Analysis

3. Data Engineering

4. Deep Learning

5. Big Data Technologies

6. Data Architecture

7. Data Processing Frameworks

- Understands the basic fundamentals of Technical Documentation and can demonstrate in common scenarios with some guidance

Qualifications & Requirements :

- BSc/MSc/PhD in computer science, data science or related discipline with 5+ years of industry experience building cloud-based ML solutions for production at scale, including solution architecture and solution design experience

- Good problem solving skills, for both technical and non-technical domains

- Good broad understanding of ML and statistics covering standard ML for regression and classification, forecasting and time-series modelling, deep learning

- 4+ years of hands-on experience building ML solutions in Python, incl knowledge of common python data science libraries (e.g. scikit-learn, PyTorch, etc)

- 2+ years of experience with simulation methods (Monte Carlo, Discrete Event or Agent Based) and respective Python libraries (e.g. SimPy)

- Strong foundational experience with Reinforcement Learning and multi-agent systems for decision-making in dynamic environments.

- Hands-on experience building end-to-end data products based on AI/ML technologies

- Experience with collaborative development workflow : version control (we use github), code reviews, DevOps (incl automated testing), CI/CD

- Expertise in neural networks, optimization techniques and model evaluation is a plus, as well as experience with LLMs, Transformer architectures (BERT, GPT, LLaMA, Mistral, Claude, Gemini, etc.). Proficiency in Python, LangChain, Hugging Face transformers, MLOps

- Team player, eager to collaborate and good collaborator

Preferred Experiences :

In addition to basic qualifications, would be great if you have :

- Hands-on experience with common OR solvers such as Gurobi

- Experience with a common dashboarding technology (we use PowerBI) or web-based frontend such as Dash, Streamlit, etc.

- Experience working in cross-functional product engineering teams following agile development methodologies (scrum/Kanban/)

- Experience with Spark and distributed computing

- Strong hands-on experience with MLOps solutions, including open-source solutions.

- Experience with cloud-based orchestration technologies, e.g. Airflow, KubeFlow, etc

- Experience with containerization (Kubernetes & Docker)