AI/ML Engineer

Key Responsibilities Develop, train, test, and deploy machine learning models for classification, prediction, recommendation, NLP, or computer vision use cases. Build scalable data pipelines and model training workflows using Python and modern ML frameworks. Collaborate with data engineers, product managers, and software developers to deliver end-to-end AI solutions. Conduct data exploration, feature engineering, and model evaluation using statistical techniques and ML best practices. Implement MLOps practices including model versioning, monitoring, CI/CD, and automated deployment. Optimize models for performance, latency, and scalability in production environments. Work with cloud platforms (AWS/GCP/Azure) to manage compute, storage, and model-serving infrastructure. Document models, experiments, and processes to ensure reproducibility. Stay current with emerging AI/ML research, tools, and industry trends. Required Skills & Qualifications Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or related field. Strong proficiency in Python and libraries such as TensorFlow, PyTorch, Scikit-learn, NumPy, and Pandas. Experience developing ML models end-to-end including data preparation, training, and evaluation. Solid understanding of algorithms, statistics, and machine learning fundamentals. Hands-on experience with cloud ML services (AWS Sagemaker, GCP Vertex AI, Azure ML). Knowledge of software engineering principles: version control (Git), testing, CI/CD. Familiarity with MLOps tools such as MLflow, Kubeflow, or DVC. Ability to work with large datasets and distributed systems. Preferred Qualifications Experience with deep learning architectures (CNNs, RNNs, Transformers). Knowledge of NLP, computer vision, or generative AI techniques. Exposure to containerization (Docker, Kubernetes). Experience building real-time inference systems or model APIs. Published work, Kaggle participation, or open-source contributions in ML/AI.