Ml Engineer

3 days ago

Hyderabad, Telangana, India Systechcorp Inc Full time ₹ 6,00,000 - ₹ 18,00,000 per year

Role & responsibilitiesML ENGINEER - Production MLOps & Model DeploymentPreferred candidate profileML ENGINEER - Production MLOps & Model DeploymentPosition Title: ML Engineer - Vertex AI Pipeline SpecialistPosition Summary: The ML Engineer will operationalize Clients predictive models through robust MLOps pipelines on GCP Vertex AI. This role focuses on model deployment, monitoring, and CI/CD integration to ensure scalable, reliable analytics solutions for lead scoring, pricing optimization, and retention modeling within Client's semi-Agile delivery framework.Core Responsibilities:Build end-to-end MLOps pipelines using Vertex AI Pipelines and Feature StoreImplement model versioning, A/B testing, and canary deployments via Vertex AI endpointsDevelop automated retraining workflows triggered by data drift detectionOptimize BigQuery queries and implement materialized views for ML feature servingContainerize models using Docker and deploy via Vertex AI or Kubernetes EngineIntegrate model serving APIs with Client's institutional applications (CRM, LMS)Configure model monitoring dashboards tracking accuracy, bias, and business KPIsMaintain CI/CD pipelines with Git integration and Azure DevOps sprint alignmentDocument deployment processes and provide knowledge transfer to Client internal teamsRequired Technical Skills:Vertex AI: Custom containers, pipelines, endpoint management, model monitoringBigQuery: Performance optimization, scheduled ML jobs, data versioningPython: FastAPI/Flask for model serving, MLflow for experiment trackingMLOps Tools: Kubeflow, TFX, or Vertex-native equivalentsCI/CD: GitHub Actions, Cloud Build, Terraform for IaCContainerization: Docker, Kubernetes, Google Kubernetes EngineProduction Excellence:Experience deploying models serving >1K predictions/secondModel governance and compliance frameworks (bias detection, fairness)Disaster recovery and model rollback proceduresCost optimization for GCP ML workloadsQualifications:Bachelor's/Master's in Computer Science, ML Engineering, or related5+ years MLOps/production ML experienceGoogle Cloud Professional ML Engineer certification requiredDemonstrated success deploying education or customer analytics at scale

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