Key Responsibilities
· Design, build, and deploy advanced ML models (regression, classification, NLP, etc.).
· Architect and implement deep learning pipelines using PyTorch or TensorFlow.
· Develop and fine-tune LLM-based applications (e.g., RAG pipelines, prompt engineering, embeddings).
· Lead and mentor junior data scientists and drive code quality and reuse.
· Apply MLOps practices: CI/CD for ML, model monitoring, reproducibility.
· Write basic SQL queries to support data processing and feature engineering.
· Collaborate cross-functionally with product, engineering, and data engineering teams.
Requirements
· 5+ years of experience in machine learning and AI.
· Expert in Python, data science libraries, and deep learning frameworks.
· Strong knowledge of regression, classification, optimization algorithms, and evaluation metrics.
· Hands-on experience with PyTorch or TensorFlow (preferably both).
· Proven experience working with LLMs and prompt-based models.
· Experience in MLOps and deploying ML models to production.
· Basic proficiency in SQL for querying structured data.
· Proficient in using Git/GitHub for collaborative development.
Good to Have
· Experience building or integrating AI agents using LangChain, AutoGen, or similar tools.
· Knowledge or experience with reinforcement learning algorithms.
Activities:
1. Data Engineering -code optimization
2. Feature Eng -code optimization
3. Model training -code optimization
4. API creation, make it deployable
5. Audit, logging, perf & drift handling
6. Deployment and Support