The Job Overview
We are looking for a skilled Artificial Intelligence Engineer to join our dynamic team. You will collaborate with software engineers, data scientists, and machine learning experts to develop innovative AI models and algorithms to solve real-world challenges and shape the future.
Your responsibilities include collecting and analysing data, identifying patterns, and building predictive models. You will design, develop, and deploy advanced machine learning solutions to extract insights, automate processes, and deliver scalable AI systems. Your expertise in model training, optimization, and problem-solving will drive impactful outcomes for our organization.
A passion for AI, machine learning, and staying updated on emerging technologies is essential.
Key Responsibilities
- Model Development and Training
- Build and train advanced machine learning (ML) and deep learning (DL) models.
- Optimize model performance using techniques like hyper parameter tuning, feature engineering, and regularization.
- Develop and maintain pipelines for model training and deployment.
- Data Management
- Preprocess, clean, and analyze large datasets to extract meaningful features.
- Handle structured and unstructured data (text, images, video, and sensor data).
- Collaborate with data engineers to ensure data availability and integrity.
- Problem-Solving
- Identify and define real-world business problems that can be addressed using AI.
- Develop and deploy AI-driven solutions to solve these problems effectively.
- Deployment and Scaling
- Deploy AI models to production environments using tools like Docker, Kubernetes, and cloud platforms (AWS, GCP, Azure).
- Ensure scalability, efficiency, and robustness of AI solutions.
- Monitor model performance in production and implement retraining pipelines.
- Convert AI/ML models into APIs that other developers can use
- Documentation
- Create detailed documentation of AI models, algorithms, and workflows, including:
- Model architecture and development process.
- Configuration and deployment steps.
- Maintenance and troubleshooting guidelines.
- Maintain a knowledge base for AI-related projects and updates.
- Training and Knowledge Transfer
- Develop training materials, including user guides and presentations.
- Conduct workshops and training sessions for technical teams and end-users to ensure effective use and understanding of AI systems.
- Provide ongoing support to address questions and challenges related to AI adoption.
- Research and Innovation
- Stay updated with the latest advancements in AI/ML.
- Experiment with emerging technologies like LLMs, generative AI, and reinforcement learning to solve complex challenges.
- Collaboration and Communication
- Work with other machine learning engineers, data engineers, and AI engineers to develop machine learning models
- Communicate findings, progress, and challenges effectively to both technical and non-technical stakeholders.
