Job Title

AI Engineer – AI Managed Services & DevelopmentAbout CentrilogicCentrilogic is a global provider of Cloud, Data, AI, and Managed Services. We help organizations modernize their systems, adopt secure and scalable AI/ML architectures, and operationalize intelligent platforms that drive measurable business outcomes.Position SummaryThe AI Engineer – AI Managed Services & Development is a production-focused engineering role responsible for supporting, operating, and enhancing AI platforms and LLM-powered applications built on Microsoft Azure AI Foundry, Azure OpenAI, and the wider Azure ecosystem.This position is centered around AI Managed Services—ensuring reliability, security, performance, cost-efficiency, and governance of customer AI workloads—while also contributing to light-to-moderate development and enhancement work in Python to improve operational efficiency and enable continuous evolution of AI solutions.Key ResponsibilitiesAI Managed Services OperationsMonitor and support AI agents, LLM workloads, vector/RAG pipelines, and microservices in production.Maintain managed service expectations and SLAs across availability, performance, response times, and issue resolution.Perform incident triage, troubleshooting, debugging, and root cause analysis (RCA).Support model and prompt lifecycle activities: drift detection, prompt updates, embedding refresh, evaluation, and version control.Apply Responsible AI practices including jailbreak protection, prompt injection defense, content filtering, and compliance guardrails.Analyze telemetry, logs, metrics, and safety signals to proactively identify and mitigate risks.Assist with onboarding new AI agents and use cases into Centrilogic’s Managed Services framework.Contribute to runbooks, SOPs, and knowledge articles for operational excellence.Development & Enhancement WorkBuild small tooling, automations, scripts, and enhancements using Python to improve service reliability and speed.Implement bug fixes, minor feature improvements, monitoring utilities, and workflow optimizations.Integrate applications and services with Azure AI Foundry and Azure AI services.Support safe deployments through CI/CD pipelines (GitHub Actions or Azure DevOps) and environment promotion.Azure Cloud & Platform ResponsibilitiesOperate AI workloads across Azure Functions, App Services, containers/AKS, API Management, Azure AI Search, and data stores (e.g., Cosmos DB, Azure SQL).Implement and maintain platform observability: logging, tracing, alerting, cost monitoring, and operational analytics dashboards.Support cloud security requirements including Key Vault, managed identities, RBAC/ABAC, encryption, private endpoints, and identity controls.Follow best practices for scalability, resilience, and operational readiness.FinOps & Operational ReportingMonitor token usage, compute cost, scaling patterns, and LLM consumption trends.Provide recommendations for cost optimization and performance improvements.Contribute input to Monthly Service Reviews (MSRs) and Quarterly Business Reviews (QBRs) with Service Delivery Managers.Client Engagement & CollaborationCommunicate operational insights, incidents, and improvements in a clear, business-friendly manner.Partner with Cloud, Data, Security, and Development teams to ensure stable and secure AI operations.Participate in architecture reviews and operational readiness assessments for AI deployments.Required Skills & Experience3–5 years of experience in application development, cloud operations, or production support (managed services experience is a plus).Proficiency in Python for troubleshooting, tooling, automations, and minor feature updates.Hands-on experience with:Microsoft Azure AI FoundryAzure OpenAI and/or Azure Cognitive ServicesAzure App Services, Functions, containers/AKS (exposure acceptable), and API integrationsLogging/monitoring tools and platform observability conceptsUnderstanding of RAG architectures, embeddings, vector databases, and prompt engineering fundamentals (practitioner-level familiarity).Experience with CI/CD (GitHub Actions or Azure DevOps) and cloud security best practices.Familiarity with incident management, RCA, and service delivery workflows (ITIL exposure is beneficial).Preferred ExperienceExperience supporting AI/ML or cloud workloads in production environments.Exposure to Salesforce, Genesys Cloud, SQL Server, Oracle, or Microsoft Fabric.Experience with Microsoft Agent Framework, Semantic Kernel, LangChain, or autonomous agent patterns.Knowledge of enterprise networking, observability tools, and SRE concepts.CertificationsRequired (or achieved within 6 months):Microsoft Certified: Azure AI Administrator (or Azure AI Engineer Associate accepted)Nice to Have:Azure Developer AssociateAzure Solutions Architect ExpertAdditional AI/ML or cloud security certificationsEducationBachelor’s degree in computer science, Engineering, Data Science, or equivalent practical experience.