AI/ML Engineer

Role Overview

The role involves feature engineering, exploratory data analysis, and developing predictive and classification models using appropriate ML algorithms. You will work with supervised and unsupervised learning techniques, implement deep learning models, and develop Generative AI and LLM-based applications using modern frameworks and tools.

Responsibilities

• Develop predictive and classification models using appropriate ML algorithms.
• Work with supervised and unsupervised learning techniques.
• Implement deep learning models using frameworks such as TensorFlow or PyTorch.
• Develop and integrate Generative AI and LLM-based applications.
• Work with technologies such as OpenAI APIs, Hugging Face, LangChain, RAG, embeddings, and vector databases.
• Build AI-powered APIs and integrate ML models with web and backend applications.
• Evaluate model performance and optimize for production deployment.
• Collaborate with cross-functional teams to integrate AI/ML features.
• Monitor deployed models and troubleshoot performance issues.

Requirements

• Strong programming knowledge in Python.
• Good understanding of Machine Learning concepts and algorithms.
• Knowledge of NumPy, Pandas, Scikit-learn, and Matplotlib/Seaborn.
• Understanding of data preprocessing, feature engineering, and model evaluation.
• Knowledge of Deep Learning and neural networks.
• Familiarity with TensorFlow or PyTorch.
• Understanding of REST APIs and basic software development practices.
• Knowledge of SQL and databases.

Skills

• Python
• TensorFlow
• PyTorch
• Scikit-learn
• LangChain

Good to Have

• Experience with Generative AI / LLMs.
• Hands-on experience with RAG, LangChain, embeddings, vector databases, and prompt engineering.
• Knowledge of OpenAI or other LLM APIs.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud.
• Knowledge of Docker and Git/GitHub.
• Experience deploying ML models into production and familiarity with MLOps.