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FNZ

Global wealth management platform for institutions

AI Platform Developer Specialist

Platform EngineeringConfirmed live in the last 24 hours
Full-timeGurugram, Haryana, IndiaNo salary listedIn-person

About the Job

Requirements

  • A bachelor's degree or higher is required, with a minimum A- average for undergraduate study and first-class standing for postgraduate study.
  • Experience deploying and operating large language model-based solutions in Azure or another major cloud provider.
  • Experience with Model Context Protocol (MCP).
  • Familiarity with AI gateways, model routing, and model management platforms.
  • Experience implementing retrieval-augmented generation (RAG) solutions, including vector databases, caching strategies, and retrieval pipelines.
  • Understanding of agent orchestration frameworks.
  • Knowledge of AI security and governance controls.
  • Experience implementing data residency and regulatory compliance requirements.
  • Experience building reusable platforms, developer self-service capabilities, and golden-path architectures.
  • Experience developing Terraform modules, providers, or reusable infrastructure frameworks.
  • Experience with HashiCorp Vault, Key Vault, or equivalent secret-management technologies.
  • Technical skills in Terraform and Infrastructure-as-Code.
  • Technical skills in Microsoft Azure; this is also listed as preferred.
  • Technical skills in Kubernetes production operations and troubleshooting.
  • Technical skills in GitHub and GitHub Actions.
  • Technical skills in OpenTelemetry and distributed tracing.
  • Technical skills in monitoring and logging platforms.
  • Technical skills in Azure networking and hybrid connectivity; this is listed as preferred.
  • Technical skills in large language models, retrieval-augmented generation, and Model Context Protocol.

Responsibilities

  • Design, develop, and maintain reusable cloud infrastructure and platform capabilities using Terraform, Azure, Kubernetes, and Infrastructure-as-Code best practices.
  • Implement and operate AI infrastructure services, including Azure AI Foundry, large language model platforms, Model Context Protocol gateways, retrieval-augmented generation solutions, vector databases, storage, and caching services.
  • Build, maintain, and optimize CI/CD pipelines, automation frameworks, and deployment processes using GitHub, GitHub Actions, and modern platform engineering practices.
  • Enable product teams to securely deploy and consume AI and cloud services through standardized platform patterns, self-service capabilities, and golden paths.
  • Implement security, identity, access control, data governance, and compliance controls for cloud and AI workloads, including management of sensitive and regulated data.
  • Establish observability, monitoring, distributed tracing, logging, and usage instrumentation to ensure platform reliability, operational visibility, and AI workload performance.
  • Drive FinOps and cost-optimization initiatives by implementing infrastructure, cloud, and AI service usage monitoring, reporting, and governance controls.
  • Support AI service and model lifecycle management, including onboarding, upgrades, deprecation planning, rollout of new capabilities, and operational support.
  • Collaborate with Product, Platform Engineering, Security, AI Governance, and Operations teams to design scalable, secure, and resilient platform solutions.
  • Evaluate emerging cloud, AI, and platform engineering technologies, contributing to continuous improvement of FNZ's Application Deployment Framework and overall platform strategy.
  • Implement infrastructure architecture designs to support agentic workloads, including models, large language model and Model Context Protocol gateways, retrieval-augmented generation, storage, and caching for agentic loops and inference.
  • Implement data governance controls, including access control and handling of personally identifiable information within retrieval-augmented generation stores.
  • Iterate infrastructure designs as agent patterns and provider offerings evolve.
  • Assist product teams with configuration and deployment of infrastructure aligned to their use cases.
  • Ensure appropriate cost and usage instrumentation, such as token and API usage, for each AI product.
  • Ensure access controls are in place around each product's large language model and tool usage.
  • Assist product teams in evidencing controls when seeking approvals.
  • Provide an escalation point for AI-related production support queries.
  • Participate in monitoring and feedback loops between AI Governance, Product, and Project teams, including supporting model version deprecation, preview evaluations, and rollout of replacement models across projects.
  • Assist InfoSec and project teams with stale or toxic data and memory cleanup as needed.

Desired Qualifications

  • Microsoft Azure experience is preferred.
  • Azure AI Foundry experience is preferred.
  • Azure networking and hybrid connectivity skills are preferred.

About the Company

FNZ

Global wealth management platform for institutions

FNZ gives large financial institutions a single, digital wealth management platform to offer wealth services. It integrates asset management, retail banking, life insurance and private banking capabilities into one online proposition that advisers and customers access via online banking. Revenue comes from platform fees, service charges, and strategic partnerships. The company differentiates itself through its scale, breadth of integrations, and track record of helping major institutions (e.g., Aviva and Abrdn) accelerate growth and transform how wealth management is delivered. Its goal is to enable institutions to provide personalized, efficient wealth management at scale, simplifying the customer experience and improving outcomes for advisers and clients.

Company size
5,001-10,000
Company stage
Growth Equity (Venture Capital)
Total funding
$3B
Headquarters
London, United Kingdom
Founded
2003

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