Machine Learning Engineer

Job Description

About the job
Key Responsibilities:

AI/ML Model Development: Design, develop, and deploy machine learning models for predictive analytics, NLP, and computer vision.
Data Preparation: Collect, preprocess, and analyze large datasets to ensure high-quality data for model training.
Model Training & Optimization: Train and optimize models using frameworks like TensorFlow, PyTorch, and Scikit-learn.
Generative AI: Implement generative AI models using tools such as GPT-3, BERT, and DALL-E.
Algorithm Development: Research and apply advanced algorithms to solve complex problems and enhance existing solutions.
Collaboration: Work with data scientists, engineers, and product managers to integrate AI solutions into products.
Performance Monitoring: Evaluate and monitor model performance to meet accuracy and efficiency standards.
Industry Awareness: Stay updated on AI/ML advancements and apply relevant innovations.
Technical Skills:

Proficiency in programming languages such as Python, R, or Java.
Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, Keras, and Scikit-learn.
Strong understanding of machine learning algorithms, including supervised and unsupervised learning, deep learning, fine tuning and reinforcement learning.
Familiarity with generative AI models and tools like GPT-4/4o, Claude, DALL-E, and StyleGAN.
Expertise in machine learning techniques and algorithms such as linear regression, logistic regression, decision trees, random forests, gradient boosting, neural networks, etc.
Hands-on experience in data preprocessing, feature engineering, and normalization techniques.
Ability to clean and manipulate large datasets for model training and evaluation.
Knowledge of natural language processing (NLP) techniques such as tokenization, word embeddings, sentiment analysis, etc., is a plus.
Develop and deploy supervised and unsupervised machine learning models (e.g., regression, classification, clustering).
Perform data preprocessing and transformation tasks (e.g., normalization, scaling, imputation).
Evaluate model performance metrics (e.g., accuracy, precision, recall, F1score) and perform model validation techniques (e.g., cross validation, bootstrapping).