Python Developer

Introduction:

The candidate is a highly skilled AI & Machine Learning Engineer with expertise in deep learning, neural networks, and MLOps, leveraging technologies like TensorFlow, PyTorch, and Keras. They have developed AI-driven solutions in diverse industries, including renewable energy, finance, and healthcare, with notable contributions such as forecasting photovoltaic (PV) energy generation and building predictive models for electricity pricing. With a strong data engineering background, they have designed and implemented ETL pipelines for real-time data processing and integrated large-scale data warehouses to enhance analytics and decision-making. Their cloud and DevOps expertise spans AWS, Azure, Kubernetes, Docker, Helm, and Terraform, ensuring efficient deployment and scalability.

In backend engineering, they are proficient in Python, Java, and TypeScript, having built microservices and distributed systems using Django, FastAPI, and Spring Boot while optimizing performance and security. Their experience includes developing financial tech solutions, such as ML-powered robo-advisory systems that leverage real-time market data for portfolio management and risk assessment. They have also played a pivotal role in security and compliance, refactoring legacy code and enhancing API security protocols. A strong advocate for CI/CD automation, they have streamlined software development workflows using Jenkins, GitHub Actions, and Bitbucket Pipelines, ensuring high-quality software delivery. Additionally, their work in AI-powered prosthetics involved translating EMG signals into digital commands, revolutionizing assistive technology. With extensive experience in mentorship and leadership, they have led technical teams, conducted code reviews, and improved hiring processes. Their ability to collaborate across global teams and industries, coupled with expertise in monitoring and observability tools like Grafana, Kibana, and CloudWatch, underscores their capability to drive innovation and efficiency in complex, data-driven environments.

Responsibilities:

• Designing and implementing ML-driven models for predictive analytics, including forecasting energy generation, financial trends, and user behavior.
• Developing deep learning algorithms using TensorFlow, PyTorch, and Keras for applications such as neural networks and clustering techniques.
• Implementing MLOps best practices for scalable deployment, monitoring, and maintenance of ML models.
• Designing and managing ETL pipelines for ingesting, processing, and storing large-scale structured and unstructured data.
• Integrating real-time data sources such as financial transactions, market data, and weather forecasts into centralized data warehouses.
• Implementing data validation, cleaning, and transformation processes to ensure data integrity.
• Developing backend services and APIs using Python, Django, FastAPI, and Java Spring Boot.
• Building scalable microservices and event-driven architectures using Kafka, RabbitMQ, and SQS.
• Refactoring legacy code to improve system performance, security, and API compliance.