Software Engineer
Summary
Builds and maintains production-ready AI services, refactors ML prototypes into scalable code, and ensures reliable data pipelines for model training and inference.
Job Description
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.
Responsibilities:
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.
Qualifications
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.
Ideal Track Record:
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.
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.
Responsibilities:
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.
Qualifications
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.
Ideal Track Record:
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.