AI Ml Engineer

Key Responsibilities

1. Develop and deploy AI/ML models for sustainability analytics, ESG data processing, and carbon footprint estimation.
2. Design predictive models for energy efficiency, renewable energy forecasting, and environmental impact assessment.
3. Process, clean, and analyze large-scale sustainability datasets (LEED, ESG reports, GRESB, CDP, Scope 1, 2, 3 emissions data).
4. Implement machine learning algorithms for lifecycle assessment (LCA) and climate risk modeling.
5. Utilize big data platforms and cloud-based AI services (AWS, GCP, Azure) for scalable solutions.
6. Automate LEED certification scoring and compliance checks using AI algorithms.
7. Optimize carbon footprint tracking and ESG risk assessment models for corporate sustainability strategies.
8. Develop APIs and AI-powered dashboards for sustainability reporting and visualization.
9. Deploy AI models using MLOps frameworks such as MLflow, Kubeflow, Docker, and Kubernetes.
10. Collaborate with data scientists, sustainability experts, and software engineers to enhance AI solutions for environmental impact.

Required Qualifications & Skills

• Education: B.Tech or B.E. in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related engineering field.
• 3-7 years of experience in AI/ML model development for sustainability, energy analytics, or ESG solutions.
• Proficiency in Python, TensorFlow, PyTorch, Scikit-learn, and XGBoost.
• Strong understanding of machine learning algorithms, deep learning architectures, and big data processing.
• Experience with data science platforms and sustainability databases (CDP, SASB, GRI, GRESB).
• Hands-on experience with cloud computing (AWS, GCP, Azure) and MLOps frameworks.