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Core","optional":true},{"name":"Triton","optional":true}],"status":"closed","first_seen_at":"2026-11-04T00:00:00Z","employer_posted_date":"2026-11-05","last_verified_at":"2026-11-06T18:59:37Z","board_verified":false,"closed_at":"2026-11-06T18:59:37Z","days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"Key responsibilities\nDesign and maintain end-to-end ML pipelines: ingestion, feature engineering, training, validation, deployment, and retraining.\nBuild and run feature stores and training datasets with versioning, lineage, and reproducibility.\nImplement CI/CD/CT for models: automated testing, model registry, approval gates, and staged rollouts (shadow, canary, A/B).\nDeploy models for batch and real-time inference on containerized, autoscaling infrastructure.\nMonitor model performance, data drift, concept drift, latency, and cost, with alerting and retraining triggers.\nBuild scalable batch and streaming data pipelines that feed ML workloads, with data quality checks built in.\nManage infrastructure as code for ML environments across dev, test, and prod.\nEnforce model governance: access control, audit trails, explainability artifacts, and compliance requirements.\nHelp data scientists move from notebooks to production-grade, modular code.\nSupport GenAI/LLM workloads where applicable: prompt and version management, evaluation pipelines, and vector stores.\nRequired skills\nArea Requirement\nExperience 4+ years in data engineering (ETL/ELT, data modeling, orchestration) and 2+ years in MLOps or ML platform work\nLanguages Strong Python and SQL; Scala or Java a plus\nArea Requirement\nData processing Spark/PySpark and distributed processing at scale\nOrchestration Airflow, Kubeflow Pipelines, or similar\nML lifecycle MLflow, Vertex AI, SageMaker, or Azure ML (registry, experiment tracking)\nContainers Docker and Kubernetes\nCI/CD GitHub Actions, GitLab CI, Jenkins, or Cloud Build\nInfrastructure as code Terraform or equivalent\nCloud GCP preferred (BigQuery, Dataflow, Dataproc, GCS, Vertex AI); AWS or Azure equivalents accepted\nObservability Prometheus, Grafana, Evidently, or cloud-native tooling\nNice to have\nStreaming: Kafka, Pub/Sub, NiFi, or Flink.\nFeature stores: Feast, Vertex Feature Store, or Tecton.\nModel serving: KServe, Seldon, BentoML, or Triton.\nLLMOps: LangChain or LlamaIndex, vector databases, and LLM evaluation frameworks.\nExperience in telecom, financial services, or other regulated, high-volume environments.\nCertifications: GCP Professional ML Engineer or Data Engineer, AWS ML Specialty.\nWhat success looks like in the first 6 months\nModel deployment time cut from weeks to days through automated pipelines.\nEvery production model has versioned data, code, and artifacts, plus drift monitoring.\nA reusable ML pipeline template adopted by at least two data science teams.\nEducation\nBachelor's in Computer Science, Engineering, or a related field, or equivalent 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