Description

Key Responsibilities
•    Perform data analysis, feature engineering, data preprocessing, and statistical modelling. 
•    Handle missing values, outlier detection, anomaly detection, and data quality issues. 
•    Build supervised and unsupervised machine learning models. 
•    Apply model evaluation and hyperparameter tuning techniques.
•    Implement ensemble learning approaches such as: Random Forest, XGBoost.
•    Build, Train and evaluate deep learning models (CNNs, RNNs, LSTMs, GRUs) / transfer learning models on structured and unstructured datasets using PyTorch / TensorFlow
•    Design NLP applications using embeddings, BERT, GPT, and modern Transformer architectures. Difference between traditional Vs modern techniques.
•    Develop and evaluate RAG pipelines using vector databases and embedding models.
•    Understanding and Implementation: Prompt Engineering, Few-shot Prompting, Chain of Thought (CoT), ReAct Framework.
•    Build AI agents and workflows using LangChain and LangGraph.
•    Fine-tune LLMs using LoRA, QLoRA, and Hugging Face frameworks.
•    Deploy AI solutions on cloud platforms and support MLOps practices.

Required Skills
•    Python, Scikit-Learn, Pandas, Numpy, Computer Vision, SQL
•    Machine Learning  & Statistics
•    Deep Learning (TensorFlow/PyTorch)
•    NLP (NLTK, SpaCy), Hugging Face & Transformers
•    Generative AI, LLMs, Prompt Engineering
•    RAG, Embeddings, Vector Databases
•    LangChain, LangGraph, Agentic AI, MCP
•    LoRA, QLoRA, Model Fine-tuning, PEFT
Preferred Experience
•    End-to-end AI/ML project development
•    Enterprise GenAI and RAG applications
•    Multi-agent AI systems
•    Model deployment and monitoring

Key Skills

Education

Bachelor's degree