Celebal Technologies - Data Scientist - AI/ML System

Description :

Department : AI & Data Science

About the Role :

We are looking for an experienced AI/ML Engineer with strong expertise in Machine Learning, Deep Learning, and Agentic AI systems.

The ideal candidate should have hands-on experience building intelligent systems that combine predictive modelling, deep learning architectures, and autonomous AI agents.

This role involves end-to-end ownership of AI solution development from data exploration and modelling to deployment and optimization.

Key Responsibilities :

- Design, build, and optimize Machine Learning models (classification, regression, clustering, recommendation systems, forecasting, etc.)

- Develop and implement Deep Learning models (ANN, CNN, RNN/LSTM, Transformers, etc.) for structured and unstructured data

- Build and deploy Agentic AI systems, including:

- Autonomous task planning

- Multi-step reasoning workflows

- Tool integration and orchestration

- Develop LLM-based applications and integrate them into intelligent agent pipelines

- Perform advanced data querying and manipulation using SQL

- Write clean, scalable production code in Python

- Evaluate model performance and implement continuous improvement strategies

- Collaborate with engineering and product teams to translate business problems into AI-driven solutions

Required Skills :

- Strong hands-on experience in Python (NumPy, Pandas, Scikit-learn, etc.)

- Proficiency in SQL for data extraction and transformation

- Deep understanding of Machine Learning algorithms (XGBoost, Random Forest, SVM, Gradient Boosting, etc.)

- Experience with Deep Learning frameworks (TensorFlow / PyTorch / Keras)

- Practical experience with LLMs and Agentic AI frameworks (LangChain Agents, LlamaIndex, CrewAI, AutoGen, etc.)

- Strong knowledge of model evaluation metrics and validation techniques

- Experience deploying ML/DL models in production environments

Nice-to-Have :

- Experience with vector databases and embedding models

- Familiarity with cloud platforms (Azure / AWS / GCP)

- Knowledge of MLOps and containerization (Docker / Kubernetes)