Full Stack Engineer (Python & AI)

Responsibilities

• Leading data science initiatives to transform raw data into actionable insights for informed decision-making
• Guiding teams in the development and deployment of advanced data solutions using machine learning and artificial intelligence
• Utilizing statistical modeling and predictive analytics to address complex business challenges
• Designing and implementing data pipelines and data lakes to support large-scale data processing
• Overseeing the creation of data visualizations to communicate insights effectively to stakeholders
• Managing project timelines and budgets to deliver data-driven solutions on schedule
• Coaching team members to enhance their skills in data science and analytics
• Collaborating with business leaders to identify opportunities for leveraging data to drive growth
• Promoting the adoption of innovative technologies and methodologies in data engineering and analytics
• Confirming data quality and compliance within analytics frameworks to maintain integrity

What You Must Have

• At least a Bachelor's degree
• At least 7 to 11 years of experience
• Oral and written proficiency in English required
• Strong full-stack or backend development skills, including Python, Angular or React, RESTful APIs, microservices, SQL/NoSQL databases, testing, and software architecture.
• Hands-on experience building AI-integrated applications, using LLMs, RAG, embeddings, vector databases, and frameworks such as LangChain, LlamaIndex, or Autogen.
• Proven cloud and DevOps capability, including Azure, AWS, or GCP deployment, Docker, Kubernetes, CI/CD, Git, security, reliability, and scalability.

What Sets You Apart

• Preference for at least one of the following fields of study: Artificial Intelligence and Robotics, Business Analytics, Computer and Information Science, Computer Engineering, Computer Programming, Data Processing/Analytics/Science, Engineering, Information Technology, Machine Learning, Management Information Systems, Mathematics, Statistics, Systems Engineering
• Demonstrating proficiency in data science algorithms and workflows
• Utilizing machine learning and deep learning techniques effectively
• Excelling in complex data analysis and predictive modeling
• Managing data pipelines and confirming data quality
• Leveraging programming languages such as Python, R, and MATLAB
• Embracing change and innovation in technology-driven environments