We are seeking a skilled and innovative AI/ML Engineer to join our team. You will be responsible for designing, developing, and deploying machine learning models and AI-driven solutions to solve complex business problems. The ideal candidate has a deep understanding of machine learning algorithms, data processing, and model deployment in production environments. Candidate should have very strong Knowledge in Python and havndle the project individually. 

Key Responsibilities:

• Develop and implement ML models for classification, regression, clustering, NLP, or computer vision tasks.
• Preprocess and clean large datasets for training and evaluation.
• Design and optimize algorithms to improve model accuracy and performance.
• Deploy ML models into production using frameworks like TensorFlow Serving, TorchServe, or cloud services.
• Monitor and maintain models post-deployment to ensure performance and accuracy over time.
• Conduct experiments and A/B tests to evaluate model effectiveness.
• Stay updated with the latest research in AI/ML and incorporate best practices.
• Collaborate with cross-functional teams (data engineers, product managers, and software developers) to integrate AI/ML solutions.

Required Qualifications:

• Bachelor’s/Master’s degree in Computer Science, Data Science, AI/ML, or related fields.
• Strong understanding of machine learning algorithms (e.g., SVM, Random Forest, XGBoost, Neural Networks).
• Experience with deep learning frameworks like TensorFlow, PyTorch, or Keras.
• Proficiency in Python and libraries such as NumPy, Pandas, scikit-learn.
• Hands-on experience with cloud platforms (AWS, GCP, Azure) for ML model deployment.
• Knowledge of data preprocessing, feature engineering, and data visualization tools.
• Experience with version control (Git) and CI/CD pipelines for ML workflows.
• Familiarity with MLOps tools like MLflow, Kubeflow, or SageMaker is a plus.

Preferred Qualifications:

• Experience in Natural Language Processing (NLP) or Computer Vision (CV) projects.
• Knowledge of reinforcement learning or generative AI (GANs, LLMs).
• Understanding of big data tools like Spark, Hadoop, or Kafka.
• Awareness of AI ethics and bias mitigation techniques.