Full Stack Developer / Artificial Intelligence

Full Stack Developer / Artificial Intelligence

The ideal candidate will be a full stack developer with experience in AI and machine learning applications. This role involves developing and deploying advanced AI tools that enhance our business processes and drive innovation.

Key Responsibilities:

• Design, develop, and implement AI tools to solve complex business problems.
• Build and maintain both front-end and back-end components of AI applications.
• Create intuitive and responsive user interfaces using frameworks like Angular.
• Work closely with the Data Science team to understand project requirements and translate them into technical solutions.
• Deploy machine learning models into production environments, ensuring scalability and reliability.
• Develop and maintain data pipelines to support AI and Data Analytics initiatives.
• Stay up-to-date with the latest advancements in AI and machine learning technologies and apply them to ongoing projects.
• Optimize application performance and model inferences to ensure efficient operation.
• Maintain comprehensive documentation for all models, algorithms, and processes.
• Participate in code reviews to ensure high-quality code and adherence to best practices.

Required Qualifications:

• 5+ years working experience with a Bachelors or Masters degree in Computer Science or a related technical field.
• Proven experience as a full stack developer using Python and JavaScript.
• Proficiency in UI development using frameworks like Angular or React.
• Experience with data engineering tools (e.g., Airflow).
• Knowledge of data structures, algorithms, and software design principles.
• Excellent problem-solving and analytical skills.
• Strong communication skills and the ability to work effectively in a team environment.

Preferred Qualifications:

• Familiarity with one or more of the following domains.
• Natural language processing (NLP) and LLMs.
• Machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn.
• Cloud platforms (e.g., AWS, GCP, Azure) and their AI/ML services.
• Big data technologies (e.g., ElasticSearch, Athena, vector DBs).
• MLOps practices and tools (e.g., SageMaker).