Your roles and responsibilities include:
- Collaborating with business users to understand their key priorities and use cases; proposing and developing solutions using data science and/or Generative AI techniques to drive business value
- Researching emerging AI/data science techniques and identifying relevant ones for EDB to explore and adopt (e.g. Agentic, LLM, Predictive, Fraud/Anomaly Detection, Text Analytics, Customer Segmentation)
- Data wrangling & analysis - preprocessing, cleaning and feature engineering
- Supporting the daily operations and maintenance of deployed data science models and products, including Assistants on PAIR / AIBots
- Developing backend APIs and services to support AI model deployment and integration
- Building frontend interfaces and user experiences for AI-powered applications
- Documenting changes to existing products
- Reviewing and implementing fixes for reported security vulnerabilities;
JOB REQUIREMENTS
To perform this role, you must have/ be:
- Minimum of Bachelor’s Degree in Computer Science, Computer Engineering, Machine Learning / Data Science / AI or related disciplines;
- Able to understand and apply a range of AI/ML techniques for regression and classification
- Familiar with popular python packages ( e.g. pandas, matplotlib, scikit-learn, XGBoost, NLTK, spaCy )
- Understanding of LLM concepts (e.g. context windows, embeddings, chunking, token management) and architectures (e.g. RAG)
- Experience with context engineering techniques and prompt optimization strategies
- Proficient in git, SQL
- Proficient in Business Intelligence tools (e.g. Tableau, Qlik, MS PowerBI, Microstrategy)
Bonus skillsets which will allow you to excel in this role include:
- Proficient in modern programming languages (e.g. typescript, C#)
- Experience in cloud platform and services preferably in AWS
- Experience in Docker/Kubernetes
- Experience with web frameworks and full-stack development, including backend frameworks (e.g. FastAPI, Flask, Express.js) and RESTful API development, and frontend technologies (e.g. React, Vue.js, HTML/CSS, TypeScript)
- Experience with LLM frameworks (e.g. LangChain, LlamaIndex, Hugging Face Transformers)
- Knowledge of vector databases and embedding techniques (e.g. Pinecone, Chroma, FAISS)
- Understanding of AI agent frameworks and multi-agent systems
- Strong presentation skills and ability to explain technical concepts clearly to a non-technical audience
- Proficient in statistical software tools (e.g. R, SAS)
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