Artificial Intelligence/Machine Learning Engineer - Python

Key Responsibilities :

• Design, develop, and deploy machine learning and deep learning models for business-critical applications.

• Build end-to-end AI/ML pipelines, from data collection and preprocessing to model training, validation, deployment, and monitoring.

• Develop predictive analytics, recommendation engines, NLP solutions, computer vision models, and generative AI applications.

• Collaborate with business stakeholders, product managers, data engineers, and software development teams to define AI use cases and technical requirements.

• Optimize model performance, scalability, accuracy, and reliability.

• Implement MLOps best practices for model versioning, deployment, monitoring, and lifecycle management.

• Perform feature engineering, model evaluation, hyperparameter tuning, and performance benchmarking.

• Develop APIs and microservices to integrate AI models into production systems.

• Work with large datasets and distributed computing frameworks for model training and inference.

• Stay updated with advancements in AI, machine learning, large language models (LLMs), and emerging technologies.

• Document technical designs, model architectures, and deployment processes.

• Mentor junior team members and contribute to AI/ML best practices across the organization.

Required Skills & Qualifications :

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, Engineering, or a related field.

• 5-10 years of experience in AI, Machine Learning, Data Science, or related domains.

• Strong programming skills in Python.

• Extensive experience with machine learning frameworks such as Scikit-learn, TensorFlow, PyTorch, Keras, or XGBoost.

• Strong understanding of supervised and unsupervised learning algorithms.

• Experience with deep learning architectures including CNNs, RNNs, LSTMs, and Transformers.

• Expertise in data preprocessing, feature engineering, and model evaluation techniques.

• Strong knowledge of statistics, probability, and mathematical foundations of machine learning.

• Experience working with SQL and large-scale datasets.

• Knowledge of software engineering principles, version control, and code optimization.