AI/ ML Engineer

We are seeking a talented and innovative AI/ML Engineer to join our growing team. In this role, you will design, build, and deploy machine learning models and AI-driven systems that drive impactful insights and real-world outcomes. You will work closely with data scientists, software engineers, and product teams to operationalize intelligent solutions using modern cloud-based infrastructure, especially on Google Cloud Platform (Google Cloud Platform).

Key Responsibilities:

• Design, develop, and implement machine learning algorithms and predictive models.
• Build and deploy scalable ML solutions using Google Cloud AI/ML tools, including Vertex AI, Big Query ML, AI Platform, and Dataflow.
• Analyze large, complex datasets to extract insights and inform model development.
• Develop and maintain production-grade ML pipelines on Google Cloud Platform (data ingestion, preprocessing, model training, serving).
• Collaborate with cross-functional teams to understand business needs and translate them into AI/ML solutions.
• Monitor and improve model performance through continuous evaluation and retraining strategies.
• Ensure responsible and ethical AI practices including bias detection, model explainability, and compliance.
• Stay up-to-date with the latest trends in cloud-native AI, machine learning, and data engineering.

Required Qualifications:

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field.
• 2 5+ years of hands-on experience in machine learning and AI development.
• Strong experience working with Google Cloud Platform, especially with Vertex AI, BigQuery, Cloud Functions, and Cloud Storage.
• Proficiency in Python and ML libraries like scikit-learn, TensorFlow, PyTorch, and XGBoost.
• Familiarity with data wrangling tools (e.g., pandas, NumPy) and SQL.
• Understanding of ML model evaluation, versioning, and deployment best practices.
• Solid foundation in statistics, linear algebra, and probability theory.

Preferred Qualifications:

• Google Cloud Platform Professional ML Engineer or Data Engineer certification.
• Experience integrating ML models with real-time inference systems and APIs on Google Cloud Platform.
• Knowledge of MLOps and CI/CD pipelines using tools like Cloud Build, Kubeflow, and Docker.
• Background in deep learning, NLP, computer vision, or generative AI.
  Contributions to open-source ML projects or published research in AI/ML fields.