Job Purpose

Responsible for designing, building, and operating a unified machine learning infrastructure that standardizes the lifecycle of models from research to production. ridges the gap between batch-oriented data in Snowflake and real-time inference on Kubernetes (K8s), ensuring high-quality software engineering standards and reducing implementation redundancy across all data science squads.

Key Result Responsibilities

• Designs and maintains the core ML Platform architecture, integrating Snowflake/Snowpark with Kubernetes for hybrid workload support.
• Designs and delivers domain specific end to end data science products, including flight revenue forecasting, customer retention, ground operations, and others.
• Automates end-to-end ML pipelines including data ingestion, model training, and continuous deployment using Apache Airflow and GitLab CI/GitHub Actions.
  
• Builds and manages a centralized Feature Store and Model Registry within Snowflake to ensure consistency between training and serving.

Key Result Responsibilities-Continued

• Implements comprehensive observability systems for monitoring model performance, data drift, and system health using Prometheus, Grafana, and Evidently AI.
• Develops reusable FastAPI or gRPC service wrappers for real-time model serving.
  
• Establishes "paved road" workflows (CI/CD, unit testing, modular code) for the broader data science team.

Qualifications (Academic, training, languages)

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field. 
• Fluent in English Language.
• Deep expertise in machine learning, statistics, and applied modeling
• Mastery of Kubernetes, Docker, and Infrastructure as Code (Terraform).
• Expert-level Python (OOP, modular design) and SQL.
• Proficiency in serving models built with LightGBM, XGBoost, TensorFlow, and PyTorch.
• Strong understanding of production ML systems and trade-offs
• Ability to influence architectural decisions related to data, modeling, and deployment in collaboration with specialized teams
• Strong leadership and communication skills to influence engineering culture without direct authority.
• Deep understanding of aviation systems including PNRs, e-tickets, and revenue management logic (Yield and Inventory control).
• Proficient in MS Office.

Work Experience

• With a minimum of 6-8 years of experience in Data Sciernce, MLOps, Platform Engineering, or DevOps specifically for machine learning.
• Out of which a minimum of 1-2 years of experience in the aviation domain – airline, vendor.
• Strong experience designing scalable and impactful solutions
• Hands-on experience with Snowflake/Snowpark and Airflow.