ML Engineer ((GCP) – Finance Data & AI Platform)

ML Engineer (GCP) – Finance Data & AI Platform

Location: Remote Duration: 6+ Months

Position Overview

This role will help design, engineer, and operationalize scalable machine learning and AI solutions across enterprise finance platforms, including Finance, planning, forecasting, KPI intelligence, semantic modeling, and executive reporting ecosystems.

The ideal contractor will possess strong hands-on implementation expertise across ML engineering, GCP 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 GCP
• Build predictive analytics and intelligent automation capabilities for Finance
• Develop ML models supporting:
• Financial forecasting
• Variance analysis
• Cost optimization
• Operating Income prediction
• Cash flow forecasting
• Financial anomaly detection
• Develop GenAI and NLP-based finance insight capabilities

GCP AI/ML Platform Development

• Build scalable ML pipelines using:
• Vertex AI
• BigQuery ML
• Dataflow
• Dataproc
• Cloud Composer
• Pub/Sub
• Cloud Functions
• Engineer reusable feature pipelines and metric-serving frameworks
• Implement production-grade MLOps processes including:
• CI/CD automation
• Model versioning
• Monitoring
• Drift detection
• Automated retraining

Finance Data Platform Integration

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

GCP 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
• Experience supporting Finance transformation initiatives
• Experience with:
• Anaplan
• SAP Analytics Cloud (SAC)
• Tableau
• Power BI
• Sigma Computing
• Experience building AI-enabled executive reporting solutions
• Experience working in highly governed enterprise environments

Preferred Certifications

• GCP Professional Machine Learning Engineer
• GCP Professional Data Engineer
• TensorFlow Developer Certification

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

Soft Skills

• Strong communication and presentation skills
• Ability to work independently with minimal oversight
• Strong stakeholder collaboration abilities
• Strong problem-solving and analytical thinking
• Ability to operate in fast-paced enterprise programs

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