TELECOMMUTE ML Engineer (Google Cloud Platform) - Finance Data & AI Platform

This role will help design, engineer, and operationalize scalable machine learning and AI solutions across enterprise finance platforms, Finance, planning, forecasting, KPI intelligence, semantic modeling, and executive reporting ecosystems. The ideal contractor will possess strong hands-on implementation expertise across ML engineering, Google Cloud Platform data services, MLOps, feature engineering, and enterprise finance analytics. This is a highly technical delivery-focused role requiring the ability to operate independently in a large-scale enterprise environment. Key Responsibilities ML Engineering & AI Solution Delivery

• Design, develop, test, and deploy enterprise ML solutions on Google Cloud Platform.

• Build predictive analytics and intelligent automation capabilities for Finance.

• Develop ML models supporting: o Financial forecasting o Variance analysis o Cost optimization o Operating Income prediction o Cash flow forecasting o Financial anomaly detection

• Develop GenAI and NLP-based finance insight capabilities. Google Cloud Platform AI/ML Platform Development

• Build scalable ML pipelines using: o Vertex AI o BigQuery ML o Dataflow o Dataproc o Cloud Composer o Pub/Sub o Cloud Functions

• Engineer reusable feature pipelines and metric-serving frameworks.

• Implement production-grade MLOps processes including: o CI/CD automation o Model versioning o Monitoring o Drift detection o Automated retraining Finance Data Platform Integration

• Work with enterprise finance datasets from: o SAP S/4HANA o SAP FI/CO o BW/BPC o Anaplan o BigQuery o Enterprise APIs

• Develop AI-ready finance semantic datasets.

• Partner with Data Engineering and Semantic teams to optimize feature consumption. Enterprise Architecture & Governance

• Align ML solutions with enterprise architecture standards.

• Support auditability, governance, lineage, and compliance requirements.

• Ensure scalable, secure, and production-ready implementation patterns.

• Participate in architecture reviews and technical design discussions.

• 7+ years of overall experience in Data Engineering / ML Engineering.
• 4+ years of hands-on experience implementing ML solutions on Google Cloud Platform.
• Strong enterprise delivery experience in large-scale environments.
• Experience deploying ML models into production ecosystems.

• Strong understanding of scalable cloud-native architectures. Required Technical Skills Google Cloud Platform Technologies

• Vertex AI
• BigQuery / BigQuery ML
• Dataflow
• Dataproc
• Cloud Composer
• Pub/Sub
• Cloud Storage
• IAM

• Cloud Functions ML & AI Technologies

• TensorFlow
• PyTorch
• Scikit-learn
• XGBoost
• Time-series forecasting
• NLP / LLM frameworks
• Feature engineering

• Model optimization Programming & Engineering

• Python
• SQL
• PySpark / Spark
• REST APIs
• CI/CD pipelines
• GitHub / GitLab

• Terraform preferred Finance & Enterprise Data Experience Strong preference for experience with:

• SAP S/4HANA Finance
• FP&A
• Financial reporting
• Forecasting & planning
• KPI engineering
• Finance semantic models

• Enterprise data governance Preferred Experience

• CVS or healthcare industry experience preferred.
• Experience supporting Finance transformation initiatives.

• Experience with: o Anaplan o SAP Analytics Cloud (SAC) o Tableau o Power BI o Sigma Computing

• Experience building AI-enabled executive reporting solutions.

• Experience working in highly governed enterprise environments. Deliverables Expected from Contractor

• Production-ready ML pipelines
• AI/ML model deployment frameworks
• Reusable feature engineering pipelines
• Forecasting and anomaly detection models
• MLOps automation solutions
• Technical design documentation
• Architecture diagrams and implementation standards

• Knowledge transfer documentation Preferred Certifications

• Google Cloud Platform Professional Machine Learning Engineer
• Google Cloud Platform Professional Data Engineer
• TensorFlow Developer Certification