Role Purpose:
Bridge LLM-based systems and classical ML/statistical modeling to support advanced AI use cases beyond RAG.
About the Role:
This role focuses on designing and implementing intelligent systems, leveraging LLMs, agentic workflows and ML models to enhance product capabilities. You will transform complex AI concepts into reliable, user-facing features that drive automation and improve user experience.
Key Responsibilities:
- Build and deploy ML models (regression, classification, time-series, anomaly detection)
- Support hybrid AI architectures (RAG + ML + rules)
- Develop evaluation frameworks (precision, recall, statistical validation)
- Optimize embeddings, retrieval scoring, and reranking models
- Improve cost and performance (model selection, caching, batching)
- Build lightweight training pipelines
Qualifications
- Bachelor's degree in Computer Science (Tier 1/2) OR Statistics (Tier 1/2)
- Master's degree in Computer Science and/or Statistics is a plus
- 5–10 years of experience in Machine Learning & AI
- Strong proficiency in Python (NumPy, pandas, scikit-learn)
- Solid background in statistics (Hypothesis Testing, AIC, BIC, ADF, KPSS, etc.)
- Experience building ML pipelines (beyond notebooks)
- Strong understanding of LLM and RAG integration
- SQL and strong data handling skills
- Time-series modeling (ARIMA family) is a plus
- Basic experience with PyTorch or TensorFlow
- Experience with MLflow or experiment tracking tools
- Experience with embeddings and vector search
- Knowledge of XGBoost / CART models
- GitHub profile + Portfolio is required
Mandatory if Statistics background:
- Machine Learning Specialization – Stanford Online
- Python for Data Science and AI – IBM
