Overview :

We are seeking a talented AI/ML Engineer to design, develop, and deploy intelligent solutions using machine learning and artificial intelligence technologies. The ideal candidate will have hands-on experience in building ML models, data preprocessing, model deployment, and integrating AI solutions into applications for real-world business use cases.

Key Responsibilities :

- Design, develop, and deploy machine learning models for classification, regression, recommendation, NLP, or computer vision tasks.

- Perform data preprocessing, feature engineering, and exploratory data analysis to prepare datasets for modeling.

- Develop and implement algorithms, neural networks, and AI pipelines using Python, TensorFlow, PyTorch, or similar frameworks.

- Deploy ML models into production environments and integrate with applications using APIs or cloud services.

- Collaborate with data engineers, software developers, and business stakeholders to define requirements and deliver AI-driven solutions.

- Evaluate model performance using appropriate metrics, tune hyperparameters, and optimize models for scalability and efficiency.

- Stay updated with emerging AI/ML trends, research papers, and tools.

- Document models, workflows, and processes for reproducibility and knowledge sharing.

Required Skills & Qualifications :

- 3- 7 years of experience in AI/ML, data science, or related roles.

- Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or scikit-learn.

- Strong understanding of machine learning algorithms, deep learning, and neural networks.

- Experience with NLP, computer vision, recommendation systems, or reinforcement learning.

- Hands-on experience with SQL, NoSQL databases, and data manipulation libraries (Pandas, NumPy).

- Knowledge of cloud platforms (AWS, Azure, GCP) for model deployment.

- Strong problem-solving, analytical, and communication skills.

- Ability to work in cross-functional teams and deliver high-quality AI/ML solutions.

Preferred Skills :

- Experience with MLOps, CI/CD for ML, and model monitoring.

- Familiarity with big data tools (Hadoop, Spark) and streaming data pipelines.

- Knowledge of containerization and orchestration (Docker, Kubernetes) for AI workloads.

- Research experience or contributions to AI/ML publications.

- Exposure to reinforcement learning, GANs, or multimodal AI models.