Description

You will design, build, and deploy end-to-end AI and machine learning solutions, ranging from traditional statistical models to modern generative AI applications.

This role is on-site.

Responsibilities

• Perform data analysis, preprocessing, and feature engineering to address missing values, outliers, and quality issues.
• Build and evaluate supervised and unsupervised machine learning models using ensemble techniques like Random Forest and XGBoost.
• Develop deep learning models (CNNs, RNNs, LSTMs, GRUs) and apply transfer learning on structured and unstructured datasets.
• Design NLP applications using BERT, GPT, and Transformer architectures, including prompt engineering and RAG pipelines.
• Implement AI agents and workflows with LangChain and LangGraph, and fine-tune LLMs using LoRA and QLoRA.

Required Skills

• 5+ years of experience in Python, Scikit-Learn, Pandas, NumPy, and SQL.
• Strong foundation in Machine Learning, Statistics, and Deep Learning (TensorFlow or PyTorch).
• Experience with NLP libraries (NLTK, SpaCy) and Hugging Face Transformers.
• Proficiency in Generative AI, LLMs, Prompt Engineering, and RAG implementation.
• Knowledge of Vector Databases, Embeddings, LangChain, LangGraph, and Agentic AI patterns.
• Experience with Model Fine-tuning, PEFT, LoRA, and QLoRA.
• Ability to deploy AI solutions on cloud platforms and support MLOps practices.

Preferred Skills

• Experience with Computer Vision and multi-agent AI systems.
• Proven track record of enterprise GenAI and RAG application development.

Key Skills

Education

Bachelor's degree