MLOps Engineer
Core
Build and maintain production-ready ML platforms and pipelines to support complex solutions in Personalization, Search, Trust & Safety, and Logistics for millions of users.
Role type
MLOps Engineer
Builds
Scalable ML infrastructure and tooling for model development, deployment, and monitoring
Domain
Marketplace technology, second-hand economy
Deliverable
production ML models
Required skills
Python, Git, CI/CD workflows, AWS (SageMaker, Lambda, S3), Kubernetes, Kafka, vector databases, semantic search infrastructure, ML orchestration/tracking tools
Preferred skills
LLMs, RAG architectures, LangChain, LlamaIndex, Spark, Beam, Airflow, dbt, Datahub, GCP, Azure
Technologies
AWS, Kubernetes, Kafka, OpenSearch, Vertex AI, Flyte, MLFlow, Feast, Pandas, Scikit-learn, TensorFlow, Pytorch, LangChain, LlamaIndex, Spark, Beam, Airflow, dbt, Datahub
Responsibilities
Iterate and maintain the ML Platform to improve speed, reliability, and maintainability; Partner with Data Scientists to provide tooling for developing, deploying, and monitoring scalable models; Adopt and promote engineering best practices within the ML domain; Partner with Data Engineering and DevOps to align ML development with infrastructure standards; Investigate and integrate new frameworks and tools for LLMs or real-time inference
Seniority
Mid-Senior, hands-on IC