Cloud Engineering & Architecture - Senior Platform Engineer AI - Vice President

Cloud Engineering & Architecture - Senior Platform Engineer AI - Vice President

Full-Time 60000 - 80000 £ / year (est.) No working from home possible
Goldman Sachs

At a Glance

  • Tasks: Lead the design and development of cutting-edge cloud platforms and AI solutions.
  • Company: Join Goldman Sachs, a global leader in investment banking and technology innovation.
  • Benefits: Enjoy competitive pay, diverse opportunities, and a culture that values inclusion and growth.
  • Other info: Collaborate with creative minds and drive innovation in a dynamic, supportive workplace.
  • Why this job: Make a real impact by solving complex engineering challenges in a fast-paced environment.
  • Qualifications: 10+ years in software or infrastructure engineering with deep AWS expertise.

The predicted salary is between 60000 - 80000 £ per year.

At Goldman Sachs, our Engineers don’t just make things – we make things possible. Change the world by connecting people and capital with ideas. Solve the most challenging and pressing engineering problems for our clients. Join our engineering teams that build massively scalable software and systems, architect low latency infrastructure solutions, proactively guard against cyber threats, and leverage machine learning alongside financial engineering to continuously turn data into action. Create new businesses, transform finance, and explore a world of opportunity at the speed of markets. Goldman Sachs Engineers are innovators and problem-solvers, building solutions in Artificial Intelligence, risk management, big data, mobile and more.

As part of Core Engineering at Goldman Sachs, the Cloud Engineering & Architecture (CE&A) team is responsible for enabling the use of public cloud services across the firm. You will be working as part of a multi-disciplinary team responsible for researching, architecting and building a cutting-edge platform that enables Goldman Sachs Engineering teams to deploy and manage services in public cloud safely and securely. The organization is seeking highly collaborative, creative, and intellectually curious engineers who are passionate about developing and implementing cutting-edge cloud computing and AI solutions. The ideal candidate will thrive in a DevOps culture and contribute to customer-centric product development. They will work closely with cross-functional teams, and will be creative collaborators who evolve, adapt to change and thrive in a fast-paced global environment.

Responsibilities And Qualifications

  • Design, develop, and operationalize enterprise-grade cloud platform capabilities.
  • Architect scalable, resilient, and secure infrastructure solutions on AWS.
  • Architect and operationalize autonomous AI based, self-healing infrastructure.
  • Define technical standards, best practices, and reference architectures for cloud adoption across the firm.
  • Partner with engineering teams to enable seamless migration and modernization of workloads to the cloud.
  • Drive automation and infrastructure-as-code practices to improve operational efficiency.
  • Drive AI-powered FinOps and predictive resource optimization.
  • Mentor and guide engineers across teams, raising the overall technical bar.
  • Collaborate with security, networking, and compliance teams to ensure platform meets regulatory and governance requirements.
  • Evaluate emerging technologies and make recommendations for platform evolution.
  • Participate in architecture design reviews and provide technical leadership on complex initiatives.

Complementary AI Based Skills

  • Experience in designing, building, and deploying Large Language Model (LLM) orchestration frameworks (e.g., LangChain, Temporal, or custom agentic loops).
  • Ability to integrate traditional observability stacks (e.g., Datadog, Prometheus, OpenTelemetry) with AI/ML models to automate root-cause analysis, anomaly detection, and semantic log clustering.
  • Experience designing closed-loop, self-healing systems that autonomously execute recovery actions (e.g., traffic shifting, automated rollbacks, or service restarts) with built-in verification and safety guardrails.
  • Deep understanding of applying machine learning and predictive analytics to dynamically right-size cloud resources, manage spot instances, and optimize data platform workloads.
  • Ability to design algorithms that forecast workload demands and proactively scale infrastructure to prevent over-provisioning while maintaining strict SLAs.

Complementary Behaviours

  • A relentless focus on eliminating repetitive operational support and engineering friction by shifting platform operations from reactive troubleshooting to autonomous mitigation.
  • Demonstrates a disciplined approach to safety by implementing strict confidence thresholds, validation loops, and human-in-the-loop fallbacks for autonomous AI actions.
  • Treats cost optimization as a first-class architectural metric, aligning infrastructure spend directly with business value and platform efficiency.
  • Executes large-scale optimization initiatives with a meticulous, risk-mitigated approach, ensuring zero disruption to production environments or developer velocity.

Basic Qualifications

  • 10+ years of experience in software engineering or infrastructure engineering.
  • Deep hands-on expertise with AWS services (EC2, EKS, Lambda, S3, IAM, VPC, CloudFormation, CDK, etc.).
  • Strong background in platform engineering, building internal developer platforms, or infrastructure tooling.
  • Experience designing and operating large-scale distributed systems.
  • Proficiency with infrastructure-as-code tools (Terraform, CloudFormation).
  • Strong understanding of containerization and orchestration (Docker, Kubernetes).
  • Experience with CI/CD pipelines and DevOps practices.
  • Knowledge of networking, security, and identity management in cloud environments.
  • Excellent communication skills with the ability to influence technical decisions across teams.
  • Experience working in regulated industries is a plus.

Preferred Qualifications

  • AWS certifications (Solutions Architect Professional, DevOps Engineer, etc.).
  • Experience building self-service platforms for development teams.
  • Familiarity with observability and monitoring tools (Prometheus, Grafana, Datadog, CloudWatch).
  • Background in financial services or other highly regulated environments.

At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world. We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has several opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs.

Cloud Engineering & Architecture - Senior Platform Engineer AI - Vice President employer: Goldman Sachs

Goldman Sachs is an exceptional employer, offering a dynamic work environment in the heart of London where innovation and collaboration thrive. Employees benefit from comprehensive professional development opportunities, a strong emphasis on diversity and inclusion, and the chance to work with some of the brightest minds in finance. With a commitment to client success and a culture that fosters growth, joining Goldman Sachs means being part of a prestigious firm that values your contributions and supports your career aspirations.

Goldman Sachs

Contact Details:

Goldman Sachs Recruitment Team

We think you need these skills to ace Cloud Engineering & Architecture - Senior Platform Engineer AI - Vice President

Cloud Architecture
AWS Services (EC2, EKS, Lambda, S3, IAM, VPC, CloudFormation, CDK)
Infrastructure-as-Code (Terraform, CloudFormation)
Containerization and Orchestration (Docker, Kubernetes)
CI/CD Pipelines
DevOps Practices
Large Language Model (LLM) Orchestration