Senior Enterprise Software Engineer -AI Operations

Overview

Staff-level individual contributor role to architect and deliver enterprise-grade AI systems on Amazon Bedrock and Microsoft Azure AI Foundry, driving technical strategy and mentoring cross-functional teams.

What you'll do

• Own end-to-end architecture of cloud-native AI systems on AWS and Azure, from 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 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.
• Drive technical design reviews, architecture critiques, and risk assessments for high-stakes AI deployments.
• Architect enterprise applications using Amazon Bedrock as the primary LLM serving and orchestration platform.
• Build production multi-agent systems using Amazon Bedrock AgentCore, including Policy controls, episodic Memory, and AgentCore Gateway tool integrations with Salesforce, Slack, and internal APIs.
• Implement browser-based workflow automation agents with Amazon Nova Act, achieving 90%+ task reliability.
• Design and deploy RAG pipelines using Amazon Bedrock Knowledge Bases with Amazon S3 Vectors as the primary vector store.
• Apply Amazon Bedrock Reinforcement Fine-Tuning and Model Distillation for domain-specific model customization.
• Implement Amazon Bedrock Intelligent Prompt Routing and Prompt Caching to reduce inference costs.
• Apply Amazon Bedrock Guardrails for content filtering, hallucination prevention, PII redaction, and compliance controls across production LLM endpoints.
• Develop and manage Kiro-based agentic development workflows for coding and operational task automation.
• Build and deploy intelligent applications on Microsoft Foundry, the unified platform for model access, fine-tuning, evaluation, and agent deployment.
• Integrate and orchestrate frontier models through Foundry Models, including Azure OpenAI, reasoning models, and Anthropic Claude.
• Design and operate multi-agent systems using Microsoft Agent Framework, deployed via Hosted Agents and published to Microsoft 365 Copilot.
• Build agentic workflows with Azure AI Agent Service, connecting agents to Azure AI Search, Azure API Management, and Azure Event Hubs.
• Leverage Microsoft Foundry Control Plane for fleet-wide agent observability, governance, policy enforcement, and compliance auditing.
• Implement Deep Research agents in Microsoft Foundry Agent Service embedded in multi-agent workflows.
• Use Foundry Tools to accelerate development of intelligent agents.
• Apply Azure AI Foundry Model Router to dynamically route prompts to the optimal model while preserving quality SLAs.
• Configure NSP-protected private deployments using VNETs, private endpoints, and BYO Key Vault connections for regulated-industry AI workloads.
• Architect AWS-native training infrastructure using EC2 Trainium3 UltraServers for large-scale model training and inference.
• Build and manage containerized model serving on Amazon EKS with Karpenter autoscaling and Triton Inference Server.
• Implement serverless inference using AWS Lambda Managed Instances for on-demand LLM endpoint invocations.
• Establish IaC-driven ML platform provisioning using AWS CDK (Python) and Azure Bicep/Terraform.
• Design cross-cloud observability stacks with unified dashboards for model drift detection, latency SLOs, and cost alerting.
• Implement MLflow for experiment tracking, model versioning, and lifecycle management across AWS and Azure workloads.
• Champion cloud cost optimization for AWS and Azure workloads.
• Define and own the organization's AI evaluation framework measuring accuracy, fairness, bias, toxicity, hallucination rate, and safety across 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.
• 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).
• Serve as principal technical mentor for mid and junior AI engineers through code reviews, architecture sessions, and 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 the AI roadmap with business objectives.
• Drive org-wide knowledge sharing through internal tech talks, open-source contributions, and certification mentorship programs.
• Lead technical hiring: define role requirements, design interview loops, and make recommendations for senior and staff-level candidates.

What you'll need

• 6-8 years of experience in AI engineering.
• Experience architecting enterprise-grade AI systems at scale, with a primary focus on Amazon Bedrock and Microsoft Azure AI Foundry.

Nice to have

• Not stated in the source.

Details

• Location: Chennai, Neville Towers, India.
• Applicants may be required to appear onsite at a Wolters Kluwer office as part of the recruitment process.
• Candidates are asked to participate in interviews without AI tools or external prompts, and to remove virtual backgrounds; use of AI-generated responses or third-party support during interviews is grounds for disqualification.