We are seeking a passionate and skilled AI Engineer to join our growing team. This mid-level role is ideal for someone with a strong foundation in machine learning and AI development, who thrives in building Proof of Concept (POC) applications and deploying scalable AI solutions on cloud platforms. You'll collaborate with cross-functional teams to bring innovative ideas to life and contribute to cutting-edge projects using Google Cloud technologies.

Key Responsibilities :

- Design, develop, and implement AI/ML models and algorithms

- Build POC applications to validate AI solution feasibility and business value

- Write clean, efficient, and well-documented Python code

- Collaborate with data engineers to ensure high-quality, accessible data for model training

- Work closely with senior engineers to understand project requirements and deliver technical solutions

- Debug and troubleshoot AI/ML models and applications

- Stay current with the latest trends and advancements in AI/ML

- Utilize frameworks like TensorFlow, PyTorch, and Scikit-learn for model development

- Deploy AI solutions on Google Cloud Platform (GCP)

- Apply data preprocessing and feature engineering using Pandas and NumPy

- Leverage Vertex AI for model training, deployment, and lifecycle management

- Integrate Google Gemini for specialized AI functionalities

Required Qualifications :

- Bachelor's degree in Computer Science, Artificial Intelligence, or a related field

- Minimum 3 years of hands-on experience in AI/ML model development

- Proficient in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn)

- Strong understanding of machine learning concepts and techniques

- Experience with data preprocessing and feature engineering

- Ability to work independently and collaboratively in a team environment

- Excellent problem-solving and communication skills

- Experience with Google Cloud Platform (GCP) preferred

- Familiarity with Vertex AI is a plus

Preferred Skills :

- Experience integrating Google Gemini or similar AI APIs

- Exposure to MLOps practices and cloud-native deployment strategies

- Knowledge of model monitoring and performance optimization

- Contributions to open-source AI/ML projects or research publications