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 Responsibilitie
- sDesigns 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-Continu
- edImplements comprehensive observability systems for monitoring model performance, data drift, and system health using Prometheus, Grafana, and Evidently A
- I.Develops reusable FastAPI or gRPC service wrappers for real-time model servin
- g.Establishes "paved road" workflows (CI/CD, unit testing, modular code) for the broader data science tea
m.
Qualifications (Academic, training, languag
- es)Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related fie
- ld.Fluent in English Langua
- ge.Deep expertise in machine learning, statistics, and applied model
- ingMastery of Kubernetes, Docker, and Infrastructure as Code (Terrafor
- m).Expert-level Python (OOP, modular design) and S
- QL.Proficiency in serving models built with LightGBM, XGBoost, TensorFlow, and PyTor
- ch.Strong understanding of production ML systems and trade-o
- ffsAbility to influence architectural decisions related to data, modeling, and deployment in collaboration with specialized te
- amsStrong leadership and communication skills to influence engineering culture without direct authori
- ty.Deep understanding of aviation systems including PNRs, e-tickets, and revenue management logic (Yield and Inventory contro
- l).Proficient in MS Offi
ce.
Work Experi
- enceWith a minimum of 6-8 years of experience in Data Sciernce, MLOps, Platform Engineering, or DevOps specifically for machine learn
- ing.Out of which a minimum of 1-2 years of experience in the aviation domain – airline, ven
- dor.Strong experience designing scalable and impactful solut
- ionsHands-on experience with Snowflake/Snowpark and Airf
low.
