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.