(Services) Senior Machine Learning Engineer

At Backbase, we’re helping financial institutions unlock the value of their data by building intelligent, personalized, and adaptive experiences across every customer journey. While GenAI is redefining engagement, the foundation remains solid, production-grade machine learning — powering decisioning, scoring, personalization, and real-time automation.

As a Machine Learning Engineer – Consultant, you’ll join our fast-paced Consulting AI team and bring classical ML systems to life: from supervised learning pipelines and anomaly detection to real-time personalization and fraud models. You’ll work closely with AI leads, data engineers, and client stakeholders to deliver production-ready ML pipelines — seamlessly integrated into core banking and customer servicing environments.

If you’re passionate about shipping real-world machine learning solutions at scale — not just notebooks — this role gives you the opportunity to drive measurable impact around the globe.

What you'll do

You will be part of our Consulting AI team and act as a senior technical contributor to AI engagements. You’ll work directly with innovative banks to help shape and deliver high-impact AI initiatives using both Backbase and non-Backbase technologies. This is a hybrid role combining solution design, engineering implementation, and delivery execution. You’ll develop AI systems — from classical ML to modern GenAI solutions — while working alongside a high-performing, multidisciplinary team.

Responsibilities:

• Design, implement, and scale supervised and unsupervised ML pipelines for real-world banking applications
• Prepare clean, feature-rich datasets from structured and semi-structured sources, with support from data engineering
• Train, evaluate, and optimize models using frameworks like scikit-learn, XGBoost, and MLflow
• Deploy models into production via containerized APIs, Databricks workflows, or serverless endpoints
• Automate model CI/CD workflows with reproducibility, version control, and rollback strategies using MLflow and Databricks Jobs
• Integrate with downstream systems: rule engines, microservices, orchestration frameworks, or GenAI components
• Implement observability, monitoring, and drift detection for deployed models

Apply responsible ML principles: fairness, explainability, risk mitigation, and compliance

• Collaborate with cross-functional teams including data engineers, platform architects, and client stakeholders
• Contribute to reusable components, documentation, and capability development initiatives

Who you are

• Strong fundamentals in machine learning: supervised/unsupervised learning, model selection, feature engineering, evaluation
• Proficient in Python with experience using scikit-learn, XGBoost/LightGBM, pandas, MLflow
• Comfortable working in Databricks and Spark-based environments

Solid understanding of MLOps best practices: CI/CD for ML, reproducibility, monitoring, model lifecycle using MLflow

• Familiarity with explainability frameworks (e.g., SHAP, LIME) and fairness/bias analysis
• Experience deploying models in production, integrating with APIs, microservices, or message brokers
• Familiarity with regulated industry concerns around privacy, risk, and auditability
• Comfortable working with cross-functional teams in consulting, client engagements, or enterprise delivery
• Able to articulate ML trade-offs to both technical and non-technical stakeholders

Requirements:

• 5+ years in ML engineering or applied data science, with a focus on production-grade model delivery
• Proven experience with real-world applications of classical ML (e.g., personalization, churn, fraud, credit risk)
• Strong proficiency in Python, MLflow, and Databricks (notebooks, Spark, Delta Lake, Workflows)
• Hands-on experience building ML pipelines using Databricks Jobs and MLflow for training, evaluation, deployment, and monitoring
• Experience with model observability, drift detection, and explainability in live systems
• Experience integrating ML solutions into enterprise backend stacks or customer-facing systems
• Desired exposure to hybrid ML/LLM systems and secure, modular orchestration patterns
• Previous experience in consulting, system integration, or client-facing delivery is a strong plus