Software Engineer-AI/ML

Role Overview: As an AI/ML Engineer, your primary responsibility will be to build, deploy, and scale Python applications along with AI/ML solutions. You will work closely with clients and software engineers to implement machine learning models and deploy them into production environments. Your expertise in Python programming and AI/ML frameworks will be crucial in developing intelligent solutions tailored to specific business requirements. Key Responsibilities: - Expertise in Python, with a deep understanding of Flask/FastAPI. - Proficient in server programming to implement complex business logic effectively. - Understanding of fundamental design principles for scalable applications. - Design, develop, and deploy machine learning models and AI algorithms independently. - Solve technical challenges by researching and implementing innovative AI/ML solutions. - Build and maintain integration (e.g., APIs) for exposing machine learning models and facilitating system integration. - Perform data preprocessing, feature engineering, and optimization of datasets for model training and inference. - Implement, monitor, and improve model performance in production environments, focusing on scalability and efficiency. - Manage model deployment, monitoring, and scaling using tools like Docker, Kubernetes, and cloud services. - Develop integration strategies for smooth communication between APIs and other systems, troubleshooting integration issues. - Create and maintain comprehensive documentation for AI/ML projects, covering model parameters, API endpoints, and integration workflows. - Stay updated with emerging trends and technologies in AI/ML, actively contributing to the team's knowledge base. Qualifications Required: - Proficiency in Python (mandatory), R, or similar languages commonly used in ML/AI development. - Hands-on experience with TensorFlow, PyTorch, scikit-learn, or similar ML libraries. - Strong knowledge of data preprocessing, data cleaning, and feature engineering. - Familiarity with model deployment best practices using Docker, Kubernetes, or cloud platforms (AWS, Azure, Google Cloud). - Strong understanding of statistical methods, probability, and data-driven decision-making processes. - Proficient in querying databases to extract relevant data for ML projects. - Experience with ML lifecycle management tools such as MLflow, Kubeflow. - Experience with NLP frameworks (e.g., spaCy, NLTK, BERT) for language-based AI solutions. - Familiarity with image processing and computer vision techniques. - Experience with managed ML services like AWS SageMaker, Azure Machine Learning, or Google Cloud AI Platform. - Familiarity with agile workflows, including experience working with DevOps or CI/CD pipelines. Additional Company Details: (Omitted as none provided in the JD),