Job Responsibilities:

• 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)