In this role as an
Associate AIML Engineer on the Global Data and Analytics (GDA) team, you will support the development of strategic, visibility-driven recommendation systems that serve both internal stakeholders and external customers. This initiative aims to deliver actionable insights that enhance supply chain execution, support strategic decision-making, and enable innovative service offerings.
Data AI/ML (Artificial Intelligence and Machine Learning) Engineering involves the use of algorithms and statistical models to enable systems to analyse 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 modelling, computational power, and data engineering to build intelligent systems that can learn, adapt, and automate decisions.
What Ill be doing your accountabilities? - 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
- AI & Machine Learning
- Data Analysis
- Machine Learning Pipelines
- Model Deployment
Specialized Skills - To be able to understand beyond the fundamentals and can demonstrate in most situations without guidance for the following skills:
- Deep Learning
- Statistical Analysis
- Data Engineering
- Big Data Technologies
- Natural Language Processing (NPL)
- Data Architecture
- 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 modeling, 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)
- 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
- Strong foundation 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
- 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)
