About this role
<div><div style="padding:10.0px 0.0px;border:1.0px solid transparent"><div style="font-size:16.0px;word-wrap:break-word"><H2 style="font-size:1.0em;margin:0.0px"><b>Job Description</b></H2> </div><div><p><strong>Role Mission:</strong><br>To accelerate the delivery of AI/ML prototypes into production by writing clean, testable code and building scalable data pipelines.</p> <p><strong>Accountabilities:</strong></p> <ul type="disc"> <li><strong>Code Quality & Delivery:</strong> Own the delivery of well-documented, unit-tested features for AI-powered applications.</li> <li><strong>Data Pipeline Stability:</strong> Ensure the reliability of data ingestion and preprocessing workflows feeding into ML models.</li> <li><strong>Model Integration:</strong> Bridge the gap between data science notebooks and production APIs/services.</li> </ul> <p><strong>Responsibilities:</strong></p> <ul type="disc"> <li>Design, develop, and maintain backend services and APIs to serve machine learning models in production.</li> <li>Write efficient code to transform, clean, and aggregate large datasets for model training and inference.</li> <li>Collaborate with Data Scientists to refactor prototype code (Python/notebooks) into production-ready modules.</li> <li>Implement logging, monitoring, and alerting for AI services to ensure high availability.</li> <li>Participate in code reviews and contribute to engineering playbooks for AI development standards.</li> <li>Troubleshoot production issues related to model latency, data drift, or system integration.</li> </ul></div></div><div style="padding:10.0px 0.0px;border:1.0px solid transparent"><div style="font-size:16.0px;word-wrap:break-word"><H2 style="font-size:1.0em;margin:0.0px"><b>Qualifications</b></H2> </div><div><p><strong>Areas of Impact:</strong></p> <ul type="disc"> <li><strong>Scope:</strong> Feature-level ownership within a specific AI squad (e.g., Search, Recommendations, GenAI Chatbot).</li> <li><strong>Decision Rights:</strong> Authority to recommend tech stack improvements and refactoring strategies for assigned modules; escalation rights for production incidents.</li> <li><strong>Stakeholders:</strong> Data Scientists, Product Managers, Tech Lead.</li> <li><strong>Partners/Resources:</strong> Access to MLOps platform team, cloud infrastructure (AWS/GCP/Alicloud), and QA engineers.</li> </ul> <p><strong>Ideal Track Record:</strong></p> <ul type="disc"> <li><strong>Project Proof:</strong> Successfully deployed at least one personal/academic project involving an API that serves an ML model (e.g., sentiment analysis API, image classification service).</li> <li><strong>Impact Proof:</strong> Refactored a legacy script or notebook, reducing execution time by <strong>20%+</strong> or improving code coverage to <strong>80%+</strong> during an internship or academic research role.</li> </ul></div></div></div>