About the job
As an Artificial Intelligence Developer at earthlinktele, you will play a pivotal role in crafting, developing, and implementing AI solutions that address intricate business challenges. This position requires proficiency in machine learning algorithms, deep learning frameworks, and the handling of large-scale datasets to create intelligent systems that foster innovation, automation, and operational efficiency. You will work collaboratively with data scientists, software engineers, and business stakeholders to translate real-world issues into scalable AI applications.
AI/ML Model Development:
- Collaborate with senior team members to implement machine learning and deep learning models.
- Utilize supervised and unsupervised learning techniques to tackle real-life problems.
- Conduct model evaluations and assist in optimizing models, including hyperparameter tuning.
Data Preparation & Feature Engineering:
- Support data exploration, cleaning, preprocessing, and transformation for machine learning pipelines.
- Create and validate features that enhance model performance and generalization.
- Work with both structured and unstructured data, encompassing text and images.
Model Integration & Deployment:
- Package and deploy models into applications or services such as APIs and batch jobs.
- Collaborate with engineering teams to ensure smooth integration of AI components.
- Assist in testing and monitoring deployed models for accuracy and stability.
Research & Prototyping:
- Investigate and experiment with new algorithms or techniques relevant to ongoing projects.
- Stay updated on industry trends and contribute to proof-of-concept solutions.
- Document discoveries and share insights with the team.
Collaboration & Communication:
- Engage closely with data scientists, machine learning engineers, software developers, and product teams.
- Translate technical concepts into layman's terms for cross-functional stakeholders.
- Participate in code reviews, sprint planning, and daily stand-ups.
Best Practices & Documentation:
- Write clean, maintainable, and efficient code adhering to team standards.
- Maintain comprehensive documentation for datasets, models, and experiments.
- Follow internal MLOps processes for model versioning and reproducibility.
