VP – Data Products and AI Enablement

MUFG

. Design and develop scalable backend data and AI platforms for commercial banking, risk, finance, operations, and enterprise data use cases .

Posted 10/15/2026full-timeUnited StatesLead💰 $150,000 - $188,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and developing scalable backend data and AI platforms, with a strong focus on optimizing data pipelines and operationalizing AI/ML workloads across cloud infrastructure. Proficient in collaborating with cross-functional teams to translate complex requirements into effective technical solutions.

Highest-signal resume keywords

Backend Data EngineeringAI/ML Infrastructure DevelopmentDistributed Systems ExperiencePython ProgrammingData Security and Governance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringAI/LLM EngineeringProduction Data PlatformsHigh-Volume Data PipelinesDistributed Processing FrameworksModel ServingInferenceMonitoringPerformance OptimizationCloud Infrastructure

Soft Skills

Strong CommunicationStakeholder ManagementProblem-SolvingTechnical Leadership

Tools & Technologies

PythonSQLKafkaAirflowSparkKubernetesAWS/CloudAPIsMicroservicesCI/CD

Certifications & Qualifications

Bachelor's Degree in Computer Science

Industry Keywords

Commercial BankingRisk ManagementFinanceOperationsEnterprise Data

Tech Stack

Tools & technologies

AirflowAWSCloudCyber SecurityDistributed SystemsKafkaKubernetesMicroservicesPythonSparkSQL

About the role

Key responsibilities & impact

Design and develop scalable backend data and AI platforms for commercial banking, risk, finance, operations, and enterprise data use cases
Build production-grade AI infrastructure including vector search, embeddings, RAG pipelines, LLM integrations, AI agents, semantic retrieval, and intelligent data services
Develop high-volume data pipelines and distributed processing frameworks using Python, SQL, Kafka, Airflow, Spark, and cloud-native technologies
Optimize data storage, retrieval, indexing, and compute performance across structured, unstructured, and vector datasets
Develop and operationalize AI/ML workloads across cloud infrastructure, including model serving, inference, retrieval, evaluation, monitoring, and lifecycle management
Partner with data scientists, AI engineers, product managers, architects, cybersecurity, risk, and business teams to translate complex requirements into scalable technical solutions

Requirements

What you’ll need

8+ years of progressive software, backend, platform, or data engineering experience
Enterprise-scale distributed systems and production data platforms experience
Knowledge of data security, governance, high availability, disaster recovery, observability, and performance optimization
Strong communication, stakeholder management, problem-solving, and technical leadership skills
Bachelor's degree in Computer Science or a closely-related discipline, or an equivalent combination of formal education and experience
Experience with AI/LLM engineering, agentic AI, RAG and vector search, data engineering, distributed systems, Python, SQL, Kafka, Airflow, Spark, Kubernetes, AWS/cloud, APIs and microservices, AI/ML infrastructure, CI/CD, and financial services
Visa sponsorship/support is based on business needs; sponsorship/support is not anticipated for this position

Benefits

Comp & perks

Discretionary performance-based bonus and/or incentive compensation
Comprehensive health and wellness benefits
Retirement plans
Educational assistance and training programs
Income replacement for qualified employees with disabilities
Paid maternity and parental bonding leave
Paid vacation, sick days, and holidays
Hybrid work schedule: four days on-site and one day remote per week