AI/ML Engineer - Python

Responsibilities : - Collaborate with product managers, business analysts, and domain experts to understand business problems and identify opportunities for AI/ML applications. - Design and implement data ingestion, cleaning, transformation, and feature engineering pipelines to prepare data for model development. - Research, design, and implement machine learning models using various techniques (i.e., regression, classification, clustering, deep learning, NLP). - Train and evaluate models using appropriate metrics and methodologies, ensuring optimal performance and generalization. - Deploy trained models into production environments and develop monitoring systems to track model performance, identify issues, and trigger retraining as needed. - Optimize models and infrastructure for scalability, performance, and efficiency in production. - Collaborate effectively with other engineers, data scientists, and stakeholders to communicate findings, share knowledge, and contribute to team goals. - Stay up-to-date with the latest advancements in AI/ML research and technologies, and explore their potential application to our products and services. - Create clear and concise documentation for models, pipelines, and processes. Required Skills : - Programming Languages : Strong proficiency in Python and relevant AI/ML libraries (i.e., TensorFlow, PyTorch, scikit-learn, Keras, NLTK, SpaCy). - Machine Learning Fundamentals : Solid understanding of machine learning algorithms, statistical modeling, and data mining techniques. - Deep Learning (if applicable) : Experience with deep learning frameworks and architectures (CNNs, RNNs, Transformers) for specific applications like computer vision or NLP. - Data Handling : Experience working with large datasets and distributed data processing frameworks (i.e., Spark, Hadoop). - Cloud Platforms (preferred) : Familiarity with cloud platforms (i.e., AWS, Azure, GCP) and their AI/ML services (e. , SageMaker, Azure ML, Vertex AI). - Model Deployment : Understanding of model deployment strategies and tools (e. , Docker, Kubernetes, REST APIs). - Version Control : Proficiency with Git and version control systems. - Problem-Solving : Excellent analytical and problem-solving skills. - Communication : Strong written and verbal communication skills. Preferred Skills : - Experience with MLOps practices and tools for automating the ML lifecycle. - Knowledge of specific AI/ML domains (i.e., computer vision, natural language processing, recommendation systems, time series analysis). - Experience with NoSQL databases. - Familiarity with data visualization tools. - Understanding of software engineering principles and best practices. Education : - Bachelor's or Master's degree in Computer Science, Engineering, Statistics, or a related field (ref:hirist.tech)

Location: in, IN

Posted Date: 5/1/2025