Job Description :
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 fieldMinimum 3 years of hands-on experience in AI / ML model developmentProficient in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn)Strong understanding of machine learning concepts and techniquesExperience with data preprocessing and feature engineeringAbility to work independently and collaboratively in a team environmentExcellent problem-solving and communication skillsExperience with Google Cloud Platform (GCP) preferredFamiliarity with Vertex AI is a plusPreferred Skills :
Experience integrating Google Gemini or similar AI APIsExposure to MLOps practices and cloud-native deployment strategiesKnowledge of model monitoring and performance optimizationContributions to open-source AI / ML projects or research publications(ref : hirist.tech)