ML Engineer (Google Cloud Platform) – Finance Data & AI Platform
Position Overview
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.
Required Qualifications
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
Sample Finance AI Use Cases
The contractor will contribute to:
Operating Income prediction models
Financial anomaly detection
Intelligent forecasting solutions
AI-driven variance analysis
Driver-based planning intelligence
Executive insight copilots
GenAI-powered finance assistants
Automated KPI intelligence platforms
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