Artificial Intelligence Developer

Apply KR's AI governance framework in every build — content filtering via Azure AI Content Safety, DLP controls, usage audit logging, data sensitivity classification, and Conditional Access enforcement for AI-powered tools.

Implement security best practices across all AI application components — Entra ID authentication, RBAC, Managed Identity for service-to-service access, secret management via Azure Key Vault, and Private Endpoint connectivity — treating security as a first-class design requirement, not an afterthought.

Implement responsible AI practices in every deliverable — input/output validation, PII detection, content filtering, audit logging, and explainability documentation for regulated advisory contexts.

Document model behavior, known limitations, bias considerations, and risk mitigations for each AI feature delivered — producing responsible AI summaries that satisfy KR's governance requirements for client-facing deployments.

Implement secure, multi-tenant data isolation patterns in AI applications — ensuring client data, internal data, and model context are correctly scoped and never cross tenant boundaries.

Design AI application architectures with cost efficiency as a first-class concern — selecting models, deployment tiers, and inference patterns that deliver the required capability at the lowest sustainable cost.

Configure and manage Azure AI Foundry deployments — model selection, deployment slots, token quotas, rate limiting, and cost monitoring — ensuring efficient and cost-controlled model consumption.

Evaluate build vs. buy vs. local model decisions for each AI use case — weighing commercial API costs against self-hosted model operational costs, latency trade-offs, and data sensitivity requirements.

Integrate Azure Monitor and Application Insights into AI applications — instrumenting latency, token usage, error rates, and custom AI quality metrics to support ongoing cost and performance monitoring.

Produce regular AI cost reports — token consumption by application, model cost per query, and cost-per-outcome metrics — giving IT leadership visibility into AI spend and ROI.

Deploy AI applications and services on Azure using containerized architectures — Azure Container Apps, AKS, or Azure App Services — depending on workload requirements and scale.

Build and maintain CI/CD pipelines in Azure DevOps for AI application workloads — automating build, test, security scan, and deployment stages through to production.

Implement API layers for AI capabilities using Azure API Management, Azure Functions, or FastAPI — exposing AI features to front-end applications and internal integrations in a secure, versioned, and observable way.

Apply infrastructure-as-code practices (Bicep or Terraform) for AI service provisioning — ensuring environments are reproducible, auditable, and aligned with KR's Azure landing zone governance.

Build data ingestion and preprocessing pipelines that prepare structured and unstructured data for AI consumption — document parsing (PDF, Word, Excel), OCR via Azure AI Document Intelligence, and schema normalization.

Integrate AI features with KR's enterprise data sources and application ecosystem — Azure Data Lake Storage, APIs, and internal platforms — through well-designed, maintainable integration layers.

Partner with KR's Architecture team on data flow design, storage tier selection, and retrieval performance optimization for knowledge bases and document stores.

Work directly from user stories, acceptance criteria, and BA specifications — asking clarifying questions proactively and flagging technical constraints or cost implications early rather than late.

Participate actively in sprint ceremonies — planning, standups, reviews, and retrospectives — contributing estimates, surfacing blockers, and demoing completed AI features to business stakeholders with clear, non-technical explanations.

Collaborate with KR's Architecture team on architectural decisions, design reviews, model selection, and cost/security trade-offs.

Produce and maintain clear technical documentation: API references, architecture decision records (ADRs), deployment runbooks, and model integration guides — so every feature is maintainable beyond the original developer.

Mentor junior developers on AI development patterns, Azure service usage, cost optimization, and responsible AI practices.

3+ years of hands-on software development, with at least 2 years focused on AI/ML application development in a production environment.

Demonstrated experience building production applications with LLMs across multiple model families — OpenAI, Anthropic Claude, Meta Llama, Mistral, or similar.

Hands-on experience building RAG pipelines — document ingestion, embeddings, vector search, chunking strategies, and retrieval optimisation.

Experience standing up and operating local or self-hosted AI model deployments (Ollama, vLLM, LocalAI, or equivalent).

Experience using Claude Code or equivalent AI-assisted development tooling as part of everyday engineering workflow.

Python proficiency — FastAPI or Flask, async patterns, and data processing libraries.

Hands-on experience with Azure AI services — Azure OpenAI Service, Azure AI Search, Azure AI Document Intelligence, and/or Azure AI Foundry.

Familiarity with orchestration frameworks — Semantic Kernel, LangChain, or LlamaIndex.

Security-first mindset — Managed Identity, Key Vault, RBAC, Private Endpoints, and authentication/authorisation patterns in cloud applications.

Cost-conscious approach to AI architecture — model selection, token optimisation, deployment tier decisions, and cost monitoring.

Excellent written and spoken English — able to communicate technical concepts and architectural decisions clearly to US-based stakeholders, produce accurate documentation, and participate confidently in cross-regional meetings.

Azure AI certifications: AI-102 (Azure AI Engineer Associate) or AZ-204 (Azure Developer Associate)

Experience with multi-tenant SaaS application architecture and data isolation patterns.

Experience with front-end frameworks (React or similar) for building AI-powered user interfaces.

Knowledge of Azure Data Lake Storage, Azure Synapse, or Azure Data Factory for AI data pipeline integration.

Exposure to responsible AI frameworks, content safety tooling, or compliance requirements in regulated industries.

Experience with infrastructure-as-code: Bicep or Terraform

AI Models: Azure OpenAI (GPT-4o, o1, o3, embeddings), Anthropic Claude (claude-sonnet-4-6, claude-opus-4-6), Meta Llama, Mistral via Ollama / vLLM

AI Services: Azure OpenAI Service, Azure AI Foundry, Azure AI Search, Azure AI Document Intelligence, Azure AI Content Safety

Local/Self-Hosted AI: Ollama, vLLM, LocalAI — model quantisation, GGUF/GGML formats, hardware inference optimisation

Developer Tooling: Claude Code, GitHub Copilot, Azure DevOps — AI-assisted development as a standard part of the engineering workflow

Orchestration: Semantic Kernel, LangChain, LlamaIndex

Languages: Python (primary), JavaScript/TypeScript (front-end)

Infrastructure: Azure Container Apps, AKS, App Services, Azure Functions

DevOps: Azure DevOps (CI/CD, Boards), Bicep / Terraform

Security: Microsoft Entra ID, Azure Key Vault, Azure API Management, Defender for Cloud, Private Endpoints

Monitoring & Cost: Azure Monitor, Application Insights, Log Analytics, Azure Cost Management