Software Engineer

Role Mission:

To accelerate the delivery of AI/ML prototypes into production by writing clean, testable code and building scalable data pipelines.

Accountabilities:

• Code Quality & Delivery: Own the delivery of well-documented, unit-tested features for AI-powered applications.
• Data Pipeline Stability: Ensure the reliability of data ingestion and preprocessing workflows feeding into ML models.
• Model Integration: Bridge the gap between data science notebooks and production APIs/services.

• Design, develop, and maintain backend services and APIs to serve machine learning models in production.
• Write efficient code to transform, clean, and aggregate large datasets for model training and inference.
• Collaborate with Data Scientists to refactor prototype code (Python/notebooks) into production-ready modules.
• Implement logging, monitoring, and alerting for AI services to ensure high availability.
• Participate in code reviews and contribute to engineering playbooks for AI development standards.
• Troubleshoot production issues related to model latency, data drift, or system integration.

Areas of Impact:

• Scope: Feature-level ownership within a specific AI squad (e.g., Search, Recommendations, GenAI Chatbot).
• Decision Rights: Authority to recommend tech stack improvements and refactoring strategies for assigned modules; escalation rights for production incidents.
• Stakeholders: Data Scientists, Product Managers, Tech Lead.
• Partners/Resources: Access to MLOps platform team, cloud infrastructure (AWS/GCP/Alicloud), and QA engineers.

• Project Proof: Successfully deployed at least one personal/academic project involving an API that serves an ML model (e.g., sentiment analysis API, image classification service).
• Impact Proof: Refactored a legacy script or notebook, reducing execution time by 20%+ or improving code coverage to 80%+ during an internship or academic research role.