We are seeking an exceptional Senior AI Engineer with 6-8 years of experience to architect and deliver enterprise-grade AI systems at scale, with primary focus on Amazon Bedrock and Microsoft Azure AI Foundry - the two leading managed cloud AI platforms. This is a staff-level individual contributor position for engineers who can drive technical strategy, mentor cross-functional teams, and translate the latest advances in generative AI into high-impact production systems.

You will operate as a principal builder and technical authority across our most ambitious AI initiatives: multi-agent orchestration pipelines, RAG-powered knowledge systems, LLM fine-tuning workflows, and cloud-native AI infrastructure. You will own decisions that reach millions of users and set the engineering benchmark for AI excellence across the organization.

This role intentionally avoids SageMaker-centric workflows in favor of the latest serverless and managed AI services - Amazon Bedrock AgentCore, Amazon Nova, Amazon S3 Vectors, Microsoft Foundry, and Azure AI Agent Service - enabling faster iteration, lower infrastructure overhead, and access to frontier models.

Technical Leadership & AI Architecture

• Own end-to-end architecture of cloud-native AI systems on AWS and Azure, from raw data ingestion through LLM serving, evaluation, and continuous monitoring.
• Establish AI engineering standards, model governance frameworks, and cloud design patterns adopted across multiple product teams.
• Lead critical cross-platform design decisions: when to use AWS Bedrock vs. Azure AI Foundry, which foundation model best fits performance and cost constraints, and when to fine-tune vs. prompt-engineer.
• Evaluate and rapidly prototype with emerging AWS and Azure AI capabilities (Amazon Nova 2, Bedrock Reinforcement Fine-Tuning, Azure GPT-5-Codex, Foundry Agent Service) to keep the organization at the frontier.
• Drive technical design reviews, architecture critiques, and risk assessments for high-stakes AI deployments.

AWS Amazon Bedrock & Generative AI Engineering

Architect enterprise applications using Amazon Bedrock as the primary LLM serving and orchestration platform

• - leveraging the full model catalog including Amazon Nova 2 (Lite, Pro, Sonic, Omni), Anthropic Claude, Mistral Large 3, and Amazon Bedrock Marketplace models.
• Build production multi-agent systems using Amazon Bedrock AgentCore: design agent boundaries with Policy controls, implement episodic Memory for long-horizon reasoning, and configure AgentCore Gateway for secure tool integrations with Salesforce, Slack, and internal APIs.
• Implement browser-based workflow automation agents with Amazon Nova Act, achieving 90%+ task reliability for form filling, data extraction, QA testing, and enterprise UI automation workflows.
Design and deploy RAG pipelines using Amazon Bedrock Knowledge Bases with Amazon S3 Vectors as the primary vector store
• - supporting up to 2 billion vectors per index with 100ms query latencies at up to 90% lower cost than specialized vector databases.
• Apply Amazon Bedrock Reinforcement Fine-Tuning for domain-specific model customization, achieving up to 66% accuracy gains over base models without requiring labeled datasets or deep ML expertise.
• Leverage Amazon Bedrock Model Distillation to create task-specific models that run up to 500% faster and cost 75% less, with minimal accuracy trade-off.
• Implement Amazon Bedrock Intelligent Prompt Routing and Prompt Caching to reduce inference costs by up to 30% while maintaining response quality.
• Apply Amazon Bedrock Guardrails for responsible AI enforcement: content filtering, hallucination prevention, PII redaction, and compliance controls across all production LLM endpoints.
• Develop and manage Kiro-based agentic development workflows: context-aware, session-persistent autonomous agents for coding and operational task automation.

AZURE Microsoft Foundry & Azure AI Engineering

• Build and deploy intelligent applications on Microsoft Foundry (formerly Azure AI Studio), the unified platform for model access, fine-tuning, evaluation, and agent deployment at enterprise scale.
• Integrate and orchestrate frontier models through Foundry Models: Azure OpenAI GPT-4.1 (1M token context), o3 / o4-mini reasoning models, GPT-5-Codex for multimodal code reasoning, Anthropic Claude (Sonnet 4.5, Opus 4.1), and Mistral Large 3.
• Design and operate multi-agent systems using Microsoft Agent Framework (Semantic Kernel + AutoGen runtime), building stateful long-running agents deployed via Hosted Agents and published to Microsoft 365 Copilot with one-click.
• Build agentic workflows with Azure AI Agent Service, connecting agents to enterprise data via Azure AI Search (RAG), line-of-business APIs through Azure API Management, and real-time data through Azure Event Hubs.
• Leverage Microsoft Foundry Control Plane for fleet-wide agent observability, governance, policy enforcement, and compliance auditing across all deployed AI agents.
• Implement Deep Research agents in Microsoft Foundry Agent Service as composable, programmable research engines embedded in multi-agent workflows and enterprise applications.
• Use Foundry Tools (unified suite of prebuilt AI capabilities for audio, video, image, document, and text) to accelerate development of intelligent agents without rebuilding common AI tasks.
• Apply Azure AI Foundry Model Router to dynamically route prompts to the optimal model (GPT-4.1, o4-mini, Phi-4) at runtime, minimizing costs while preserving quality SLAs.
• Configure NSP-protected private deployments using VNETs, private endpoints, and BYO Key Vault connections for regulated-industry AI workloads on Foundry.

Cross-Platform Infrastructure & MLOps

Architect AWS-native training infrastructure using EC2 Trainium3 UltraServers (Trn3) - AWS's 3nm AI chip delivering 4x performance over Trn2

• - for large-scale model training and inference at lower cost.
• Build and manage containerized model serving on Amazon EKS with Karpenter autoscaling, integrated with Triton Inference Server for multi-model, multi-framework inference optimization.
• Implement fully serverless inference using AWS Lambda Managed Instances for on-demand LLM endpoint invocations without infrastructure provisioning.
• Establish IaC-driven ML platform provisioning using AWS CDK (Python) and Azure Bicep/Terraform, ensuring all AI environments are version-controlled, reproducible, and auditable.
Design cross-cloud observability stacks: Amazon CloudWatch + AWS X-Ray on AWS; Azure Monitor + Application Insights on Azure
• - with unified dashboards for model drift detection, latency SLOs, and cost alerting.
• Implement MLflow (serverless via SageMaker AI integration or self-hosted on EKS) for experiment tracking, model versioning, and lifecycle management across both AWS and Azure workloads.
• Champion cloud cost optimization: AWS Spot instance strategies and Bedrock Intelligent Prompt Routing for AWS workloads; Azure Developer Training tier and Serverless API pay-as-you-go for Azure workloads.

Evaluation, Safety & Responsible AI

Define and own the organization's AI evaluation framework

• - automated pipelines measuring model accuracy, fairness, bias, toxicity, hallucination rate, and safety across both AWS Bedrock and Azure Foundry deployments.
• Lead red-teaming, adversarial robustness testing, and jailbreak analysis using Amazon Bedrock Guardrails and Azure AI Content Safety before every production launch.
• Establish model risk management: model cards, prompt audit trails, production monitoring dashboards, and incident response runbooks for AI systems.
• Collaborate with Legal, Compliance, and the AI Ethics board to meet regulatory requirements (EU AI Act, NIST AI RMF, ISO 42001, SOC 2 Type II).

Mentorship & Organizational Influence

• Serve as principal technical mentor for mid and junior AI engineers: conducting code reviews, architecture design sessions, and cloud platform enablement workshops.
• Represent the team at executive forums, customer briefings, and external AI conferences.
• Partner with Product, Research, Data Science, and Platform Engineering to align AI roadmap with business objectives and cloud investment strategy.
• Drive org-wide knowledge sharing via internal tech talks, open-source contributions, technical blog posts, and AWS/Azure certification mentorship programs.
• Lead technical hiring: define role requirements, design AI cloud-focused interview loops, and make recommendations for senior and staff-level candidates.

Our Interview Practices

To maintain a fair and genuine hiring process, we kindly ask that all candidates participate in interviews without the assistance of AI tools or external prompts. Our interview process is designed to assess your individual skills, experiences, and communication style. We value authenticity and want to ensure we’re getting to know you—not a digital assistant. To help maintain this integrity, we ask to remove virtual backgrounds and include in-person interviews in our hiring process. Please note that use of AI-generated responses or third-party support during interviews will be grounds for disqualification from the recruitment process.

Applicants may be required to appear onsite at a Wolters Kluwer office as part of the recruitment process.