Machine Learning Engineer II
Experience: 5 to 7 Years
Role Overview
We are looking for experienced ML Engineers who can independently design, develop, deploy, and monitor scalable ML systems. You will own components of our real time AI personalization platform end to end, from feature engineering through model serving and monitoring.
Must Have Skills (all ML1 expectations are mandatory)
• Strong expertise in end-to-end ML lifecycle management
• [NEW] Experience in building scalable ML pipelines and production grade systems that serve real users at scale, not just POCs or demos
• [NEW] Experience with model observability, drift detection (PSI, CSI, KS tests), retraining strategies, and performance optimization
• Detailed understanding of data engineering concepts like ETL design, Medallion architecture, etc.
• Hands-on experience with MLOps tools (MLflow, Gitlab CI, or similar) and CI/CD practices
• Experience working with cloud platforms and distributed ML environments
• [NEW] Strong Python skills: must be able to solve medium-difficulty data manipulation and algorithm problems within 10 minutes in a live interview setting
• [NEW] Strong SQL skills: window functions, self-joins, conditional aggregation, date arithmetic, and performance reasoning
• [NEW] Experience with containerized model deployment (Docker) and model serving frameworks (Triton, TorchServe, ONNX, or FastAPI based serving)
• [NEW] Experience with feature stores or online feature serving infrastructure (Tecton ,Redis, DynamoDB, Feast, or similar)
• [NEW] Hands on experience with API development using frameworks like FastAPI, Flask, Django
• Ability to translate business problems into ML/AI solutions
• Ability to mentor junior engineers and drive technical discussions
Good-to-Have
• Hands-on exposure to Generative AI / LLM ecosystems
• Understanding of Prompt Engineering, RAG architectures, embeddings, and vector databases
• [NEW] Experience with recommendation ranking models (two-tower, GBDT rankers, wide-and-deep)
• [NEW] Experience with event streaming systems (Kafka, Kinesis) for real-time feature computation
• [NEW] Experience with A/B testing frameworks and online experimentation
• Retail and Personalization domain expertise
What this role is NOT
[NEW] This is not a consulting or POC building role. Candidates must have owned at least one ML system running in production with real users, real SLAs, and real monitoring. Demo level or hackathon-level experience does not meet the bar