Machine Learning Engineer (MLOps)

Quick Facts

• Role: Machine Learning Engineer (MLOps)

• Focus: Predictive ML models for CRM, sales, marketing, and campaigns; Google Cloud-based production lifecycle

Description

You will design, develop, and deploy machine learning models that support CRM, marketing, sales, and campaign use cases in a large, regulated financial organization. The work includes building classification/regression/NLP predictive models, developing GCP-based data and ML pipelines, and automating production CI/CD for machine learning. You will also monitor model quality, drift, and business impact, manage model versions and retraining, and help shape MLOps governance standards.

Responsibilities

• Design, develop, and deploy ML models supporting CRM, marketing, sales, and campaigns

• Build classification, regression, NLP, and other predictive solutions

• Develop models using Python, PyTorch, Spark/PySpark, and Google Cloud Platform

• Prepare and optimize training and inference data

• Build data and ML pipelines using Airflow, Dataflow, and other GCP tools

• Deploy models to production and automate ML CI/CD processes

• Monitor data quality, model effectiveness, drift, and business outcomes

• Manage model versions, retraining, and the full model lifecycle

• Identify high-predictive-value customer behaviors and events

• Optimize decision engines for lead and offer distribution

• Collaborate with Data Engineering, IT, marketing, and sales teams

• Contribute to modeling standards, validation automation, and governance in a regulated environment

• Participate in initiatives for hyper-personalization, GenAI, LLMs, and R&D experiments

Requirements

• Multi-year experience building predictive models (preferably in banking/finance or another regulated environment)

• Practical experience with CRM/sales/marketing use cases (lead scoring, next best offer, churn, cross-sell, upsell, customer analytics)

• Very strong Python and data analysis/processing skills

• Very strong SQL; preferred experience with BigQuery

• Practical experience with Google Cloud Platform

• Familiarity with BigQuery, Vertex AI, Dataproc, Dataflow, Airflow/Cloud Composer, and/or MLflow

• Experience processing large datasets using Spark/PySpark

• Experience building, deploying, and maintaining models in production

• MLOps best practices: pipeline automation, model versioning, CI/CD, monitoring, retraining

• Experience with batch or near-real-time data processing

• Ability to work with business stakeholders and translate marketing/sales needs into analytical solutions

• Higher education in a technical, mathematical, IT, economics, or related field

Benefits

• Long-term collaboration on strategic Data Science and Machine Learning solutions

• Work with very large datasets in a modern Google Cloud Platform environment

• Real impact on solutions supporting sales, marketing, and customer experience

• Participation in the full model lifecycle—from experiments to production deployment and monitoring

• Collaboration with experienced technical and business teams

• Opportunity to contribute to hyper-personalization, GenAI, and evolving MLOps standards