Job
Description
As a AI/ML Engineer, you will be responsible for designing, developing, and implementing machine learning algorithms and AI solutions that address complex business challenges. You will lead a team of engineers collaborating with cross-functional teams to drive the successful deployment of AI initiatives. Your expertise will be crucial in shaping our AI strategy and ensuring the delivery of high-quality, scalable solutions. Key Responsibilities: Lead the design and development of machine learning models and AI solutions to solve business problems. Select appropriate machine learning or deep learning models based on problem context and data availability. Develop, train, test, and validate models using state-of-the-art methodologies and frameworks. Collaborate with analytics, developers and domain experts to acquire, clean, and preprocess large datasets. Engineer features and performs exploratory data analysis to ensure data quality and model readiness. Containerize models (Docker/Kubernetes), implement monitoring (Prometheus/Grafana), and automate pipelines (MLflow/Kubeflow). Implement models into production environments, ensuring robustness, scalability, and maintainability. Develop and maintain CI/CD pipelines for seamless model integration and deployment. Monitor and evaluate model performance post-deployment and iterate based on feedback and performance metrics. Document model architecture, development processes, and performance evaluations thoroughly. Share insights and technical know-how with team members to foster a culture of continuous learning and improvement. Research & Innovation: Stay ahead of AI trends (LLMs, generative AI) and advocate for ethical AI practices. Analyze large datasets to extract insights and improve model performance. Ensure compliance with data privacy and security regulations in all AI/ML initiatives. Qualifications: Bachelor's or master's degree in computer science, Data Science, Machine Learning, or a related field. Proven experience (3+ years) in AI/ML engineering, with a strong portfolio of successful projects. Proficiency in programming languages such as Python, R, or Java, and experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). Strong understanding of machine learning algorithms, statistical modeling, and data analysis techniques. Experience with cloud platforms (e.g., AWS, Azure, Google Cloud)
