Generative AI Architect

Role & Responsibilities :

- Translate ambiguous business and product problems into well-defined data science and machine learning problems, with clear success metrics and evaluation criteria.

- Apply statistical analysis and experimentation techniques to support data-driven decision-making, including hypothesis testing, A/B testing, and causal analysis.

- Develop, evaluate, and improve machine learning models using appropriate algorithms, metrics, validation techniques, and feature engineering approaches.

- Perform complex SQL-based data analysis, investigate data quality issues, and work with structured data models to derive meaningful insights.

- Build reproducible and production-quality data science workflows using Python/R, Git, testing, documentation, and appropriate development practices.

- Own projects end-to-end, from data discovery and problem definition through modeling, validation, deployment planning, and monitoring.

- Partner with business, product, engineering, and other stakeholders to understand requirements, communicate findings, and influence data-driven decisions.

- Identify risks related to data availability, model performance, privacy, bias, and feedback loops, and proactively develop mitigation strategies.

- Communicate complex analytical results and model outputs in a clear and actionable manner to both technical and non-technical stakeholders.

- Contribute to improving data science practices, reusable frameworks, model quality, and analytical standards across the team.

Preferred Candidate Profile :

- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.

- Strong hands-on experience in Python or R, with a solid understanding of data analysis and machine learning workflows.

- Strong knowledge of statistics, experimentation, hypothesis testing, A/B testing, and causal inference.

- Strong proficiency in SQL, including complex joins, CTEs, window functions, and data validation.

- Practical experience developing and evaluating supervised machine learning models, including regression, tree-based models, boosting, and regularization.

- Good understanding of model evaluation, feature engineering, cross-validation, data leakage, imbalanced datasets, and model interpretability.

- Experience taking machine learning/data science projects from problem definition to implementation and business delivery.

- Strong analytical and problem-solving ability, with a product-oriented and business-focused mindset.

- Excellent communication and stakeholder management skills, with the ability to explain technical concepts and business impact clearly.

- Experience with Git and reproducible development practices.

Good to Have :

- Experience with MLOps, model deployment, monitoring, CI/CD, Docker, or Kubernetes.

- Exposure to AWS, Azure, or GCP cloud-based ML platforms.

- Experience with PyTorch/TensorFlow, NLP, Computer Vision, recommender systems, or transformers.

- Knowledge of Spark, Databricks, Airflow, Kafka, or other data engineering technologies.

- Experience with advanced causal inference, optimization, forecasting, or reinforcement learning.

- Relevant industry/domain experience such as fintech, healthcare, supply chain, pricing, fraud/risk, or similar areas.

- Experience mentoring team members or contributing to data science best practices and reusable frameworks.

Skills

Generative AI, Artificial Intelligence, Data Science, Python, R, Data Analytics, Machine Learning, AI Architecture, SQL, Cloud, Tensorflow