MLOps Engineer: GCP & Vertex AI for Forecasting

neteffects; West Newton (MA); On-site; USD 140,000 - 190,000; Full time

Job summary

NetEffects in the United States is seeking an experienced MLOps/ML Engineer to design and operate production ML workloads. You will build and maintain CI/CD pipelines for model training, validation, deployment, and rollback, and productionize models from data scientists and partners.

Your role includes improving time-series forecasting models, monitoring accuracy, and applying feature engineering and hyperparameter tuning to boost business outcomes.

Qualifications

• Bachelor's degree in Computer Science, Engineering, Data Science, Analytics, or related field.
• 3+ years in MLOps or production ML workloads with hands-on deployment.
• Experience deploying and operating ML models in production and improving them.
• Hands-on data science capable, able to modify features and hyperparameters.
• Experience owning time-series or demand forecasting models in production.
• Forecasting techniques and accuracy measures like WMAPE; explain tradeoffs.
• Proficiency with ML libraries, Python, testing, and production-quality code.
• GCP experience (Vertex AI, BigQuery) and CI/CD for ML pipelines.
• Containerization and orchestration (Docker, Kubernetes); IaC (Terraform).
• Workflow orchestration (Airflow/Vertex AI Pipelines) and data warehousing concepts.
• Model monitoring, drift detection, automated alerting; explain model performance.
• Bonus: LLM ops, Gemini/Vertex, prompt versioning, and retail forecasting context.

Responsibilities

• Design, build, and maintain CI/CD pipelines for model training, validation, deployment, and rollback.
• Productionize models developed by data scientists and vendors into scalable pipelines.
• Own maintenance and performance of enterprise sales forecasting models, with accuracy tracking.
• Diagnose forecast accuracy degradation and apply data science techniques to improve results.
• Evaluate forecast performance using measures like WMAPE and report trends to stakeholders.
• Translate business context into model features and adjustments for useful forecasts.
• Partner with Finance, Operations, and Supply Chain to ensure business impact of changes.
• Deploy and manage models on Vertex AI (training jobs, endpoints, batch prediction, pipelines).
• Operationalize LLM and generative AI workloads, including in-warehouse invocation from BigQuery.
• Implement model monitoring for accuracy, drift, data quality, latency, and automated alerts.
• Build automated retraining workflows with promotion criteria and approval gates.
• Establish model registry, versioning, and lineage for traceability.
• Manage feature pipelines and ensure training/serving data consistency.
• Monitor compute and inference cost; optimize model selection and real-time vs batch tradeoffs.
• Define IaC standards for ML environments and promote from development to production.
• Collaborate with Security/Privacy to meet data classification and retention requirements.
• Support governance and audit by documenting model behavior and validation results.
• Troubleshoot production failures, perform root cause analysis, and implement safeguards.
• Mentor engineers and data scientists on production standards and deployment practices.

Skills

Python programming

MLOps

Production ML

Time series forecasting

SQL proficiency

CI/CD pipelines

Model monitoring

Drift detection

GCP

Data pipelines

Education

Bachelor's degree in Computer Science, Engineering, Data Science, Analytics, or related

Tools

Vertex AI

BigQuery

Docker

Kubernetes

Terraform

Airflow

Cloud Composer

Vertex AI Pipelines

Job description

NetEffects in the United States is seeking an experienced MLOps/ML Engineer to design and operate production ML workloads. You will build and maintain CI/CD pipelines for model training, validation, deployment, and rollback, and productionize models from data scientists and partners.

Your role includes improving time-series forecasting models, monitoring accuracy, and applying feature engineering and hyperparameter tuning to boost business outcomes.