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 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
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
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
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)
