AWS Cloud Engineer responsible for engineering, operating, and evolving Santander’s AWS data lake platform, including infrastructure, shared services, and data engineering applications.
Compensation
Salary Range: £60,800.00 - £91,200.00 per annum (depending on experience)
Responsibilities
Support the engineering, operation, reliability and continuous improvement of Santander's AWS data lake.
Build, configure and support AWS platform capabilities using services such as S3, IAM, VPC, KMS, CloudWatch, CloudTrail, Glue, Athena, EMR, Lambda and Step Functions.
Develop and maintain Infrastructure as Code for AWS environments and reusable platform components.
Contribute to CI/CD pipelines and deployment automation that support safe, repeatable and controlled platform changes.
Apply approved cloud security controls, including least‑privilege access, encryption, network isolation, secrets management and policy enforcement.
Requirements
Degree in software engineering, computer science or related field
Nice to have: Agentic AI and AI‑Assisted Engineering
Conception et implémentation de systèmes data-intensive et de pipelines de récupération, de traitement et de stockage de la donnée. Développement de dashboards de datavisualisation et d’aide à la décision. Travaux d’intégration sur des plateformes BigData/IA. Modélisation de la donnée.
Responsibilities
Conception et implémentation de systèmes data-intensive et de pipelines de récupération, de traitement et de stockage de la donnée
Développement de dashboards de datavisualisation et d’aide à la décision
Travaux d’intégration sur des plateformes BigData/IA
Modélisation de la donnée
Requirements
Bac+5 en informatique (École d'ingénieur ou Master)
Bring machine learning models from development and experimentation to reliable, scalable production systems, working closely with Data Scientists, Data Engineers, Platform Engineers, and Services teams to productionize models, build robust feature pipelines, and ensure models operate reliably in live environments.
Responsibilities
Lead the deployment of ML models into production using Databricks and MLFlow
Implement scalable and reliable model serving solutions (both batch and real-time)
Design and maintain CI/CD pipelines for automated build, test, validation, and deployment of ML artifacts
Support safe rollout and rollback strategies across environments
Translate feature definitions from data scientists into production-grade pipelines
Requirements
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
Entwicklung und Betrieb von End-to-end Machine Learning und Agentic AI Lösungen, Architekturentwurf und Weiterentwicklung in einer innovativen Cloud-Umgebung.
Compensation
Faire Vergütung & volle Flexibilität: Marktgerechtes Gehalt, flexible Arbeitszeiten mit Arbeitszeitkonto und Teilzeitmodelle.
Responsibilities
Entwicklung und Betrieb von End-to-end Machine Learning Lösungen vom Training bis zum produktiven Betrieb (MLOps)
Entwicklung und Betrieb von Agentic AI Lösungen
Monitoring und kontinuierliche Verbesserung bestehender System-Architekturen für ML- und Agentic AI Anwendungen
Enge Zusammenarbeit mit Data Scientists, Data Engineers und Software Engineers
Beratung bei Architekturentscheidungen unter Berücksichtigung verschiedener Technologien wie Databricks, Cloud Umgebungen, Kubernetes und Apache Kafka
Requirements
Studium in Informatik, Data Science oder vergleichbare Qualifikation
This role is responsible for providing strategic direction and leadership for the software data engineering unit including data and analytics platforms and the overall data governance, data management platforms, advanced analytics, and shared services and platforms.
Responsibilities
Provides leadership for software data engineering, data management, information governance, enterprise information portals management, and implementation.
Drives innovation and defines strategies on approach and tools to support dashboarding, improve data quality, and enable predictive analytics.
Identifies strategies to integrate cross-business unit data for enterprise master data management, traditional reporting, data interfaces, and analytics.
Harnesses data-driven machine learning and artificial intelligence to provide a capability that can be pervasively applied across the enterprise.
Creates overall software data engineering roadmap, organizes and implements policies, principles, procedures, structures, roles, and responsibilities.
Requirements
Four-year or Graduate Degree in Computer Science
Four-year or Graduate Degree in Information Technology
Four-year or Graduate Degree in Software Engineering
Four-year or Graduate Degree in Statistics/ Mathematics
Data engineers to join the Scientific Data Foundation team, enabling biopharma industry transformation to data-centric, AI/ML-driven enterprises by building efficient data pipelines and engineering new data analytics/visualization capabilities.
Compensation
We offer a competitive compensation package including accrued vacation, medical, dental, vision, 401k with company matching, life insurance, and flexible spending accounts.
Responsibilities
Build ingestion pipelines for clinical trial protocols, ICF documents, SmPCs, CSRs, and published articles (PubMed, CTIS, ClinicalTrials.gov)
Design and implement data models in Amazon Aurora (relational) and GraphDB (knowledge graph)
Develop embedding and vectorization pipelines to prepare extracted clinical text for RAG-based retrieval in LangGraph agentic workflows
Build and maintain ETL/ELT workflows that transform unstructured clinical content into queryable, linked data across both relational and graph stores
Implement data quality validation specific to clinical data
Requirements
Bachelor’s or Master’s degree in data engineering / computer science or related field
Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability.
Locations (3)
McLean, Virginia, United States
New York, New York, United States
San Jose, California, United States
Compensation
McLean, VA: $161,800 - $184,600 for AI Engineer 3
New York, NY: $176,500 - $201,400 for AI Engineer 3
San Jose, CA: $176,500 - $201,400 for AI Engineer 3
Responsibilities
Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One.
Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc.
Lead a portfolio of diverse technology projects and a team of developers with deep experience in distributed microservices, and full stack systems to create solutions that help meet regulatory needs for the company.
Compensation
Richmond, VA: $179,400 - $204,700 for Full-stack Engineer 4
Responsibilities
Lead a portfolio of diverse technology projects and a team of developers with deep experience in distributed microservices, and full stack systems to create solutions that help meet regulatory needs for the company
Share passion for staying on top of tech trends, experimenting with and learning new technologies, participating in internal & external technology communities, mentoring other members of the engineering community
Collaborate with digital product managers, and deliver robust cloud-based solutions that drive powerful experiences to help millions of Americans achieve financial empowerment
Provide technical leadership and direction across Agile teams, setting standards for design, development, and operational excellence
Partner with architects to define service boundaries, integration patterns, and reusable frameworks
Requirements
Bachelor's Degree or higher in Computer Science or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, and more.
Invent and introduce state-of-the-art foundation model optimization techniques to improve the performance — scalability, cost, latency, throughput — of large scale production AI systems.
Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One.
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