Responsibilities Our client’s technology team is responsible for creating and continuously improving a robust and secure technology foundation that supports the firm’s business activities. Their technology team is seeking a Data and AI Infrastructure Engineer to build secure, scalable platforms and pipelines that power the firm’s AI initiatives. This is a platform engineering role — the focus is building the infrastructure and developer tooling that enables teams to rapidly develop and deploy AI-powered Python applications using Azure AI Foundry and the Claude API (Anthropic), not training custom models. You will work with data engineers, application developers, architects, and business stakeholders to deliver AI-ready infrastructure that meets the firm’s security and compliance requirements. Responsibilities include:
AI Platform & Developer Infrastructure
Design, build, and operate the firm’s AI platform, enabling developers to build and deploy Python-based AI applications;
Implement and manage Azure AI Foundry environments: model deployments, AI hubs, project workspaces, and access controls;
Integrate and operationalize third-party AI APIs (Anthropic Claude API, Azure OpenAI) with secure access patterns, API gateway controls, rate limiting, and cost monitoring;
Build internal developer tooling and SDK scaffolding to accelerate AI application development across the firm.
Data Infrastructure & Pipelines
Build and maintain data pipelines using Azure Data Factory and Azure Databricks to serve AI application data needs;
Implement vector search and document retrieval infrastructure (Azure AI Search) to support RAG-based applications;
Manage structured and unstructured data stores including Azure Data Lake, Azure SQL, and Cosmos DB.
Cloud Infrastructure & DevOps
Provision and maintain secure, scalable infrastructure on Azure (primary) and AWS using Infrastructure-as-Code (Terraform or Bicep);
Build and maintain CI/CD pipelines for AI application deployment via Azure DevOps or GitHub Actions;
Manage containerized workloads using Docker and Kubernetes (AKS) for AI application hosting and API services;
Drive Secure DevOps practices including secrets management, dependency scanning, and policy enforcement.
Security, Governance & Compliance
Implement data access controls, encryption, and audit logging aligned with SEC and FINRA requirements;
Design MNPI data segregation controls and enforce AI governance guardrails including content filtering and usage logging for all AI API integrations;
Manage identity and access for AI platforms using Azure Entra ID, RBAC, and managed identities;
Collaborate with Compliance and Legal to ensure AI usage policies are enforced at the infrastructure level.
Collaboration & Documentation
Produce architecture designs, runbooks, and technical documentation for all platform components;
Support application developers building AI-powered applications with platform guidance and troubleshooting;
Partner with solution architects, data engineers, and business stakeholders to translate requirements into infrastructure.
Qualifications Our client seeks to hire individuals who are highly motivated, intelligent and have demonstrated excellence in prior endeavors. In addition, qualified candidates will possess the following:
Bachelor’s degree in computer science, Computer Engineering, or related field (Master’s degree is a plus);
8+ years in infrastructure engineering, cloud platform engineering, or data engineering;
Demonstrated experience building shared platforms or developer services in an enterprise environment;
Azure expertise: Azure AI Foundry, Azure Data Factory, Azure Databricks, AKS, Azure API Management, Azure Key Vault, Azure Entra ID;
Strong Python skills: backend services, REST APIs (FastAPI or Flask), and automation scripting; PowerShell for infrastructure tasks;
Infrastructure-as-Code:Â Terraform and/or Bicep; container orchestration with Docker and Kubernetes;
Experience integrating LLM APIs (Anthropic Claude, Azure OpenAI) in production including token cost management and observability;
RAG pipeline experience: vector search (Azure AI Search or pgvector), document processing, and retrieval patterns;
Familiarity with LLM application frameworks such as LangChain or Semantic Kernel;
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