AI Engineer

AI Engineer – AI Managed Services & Development

About Centrilogic

Centrilogic 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 Summary

The 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 Responsibilities

AI Managed Services Operations

• Monitor 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 Work

• Build 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 Responsibilities

• Operate 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 Reporting

• Monitor 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 & Collaboration

• Communicate 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 & Experience

• 3–5 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 Foundry


• Azure OpenAI and/or Azure Cognitive Services


• Azure App Services, Functions, containers/AKS (exposure acceptable), and API integrations


• Logging/monitoring tools and platform observability concepts


• Understanding 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 Experience

• Experience 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.

Certifications

Required (or achieved within 6 months):

Microsoft Certified: Azure AI Administrator (or Azure AI Engineer Associate accepted)

Nice to Have:

Azure Developer Associate
Azure Solutions Architect Expert
Additional AI/ML or cloud security certifications

Education

Bachelor’s degree in computer science, Engineering, Data Science, or equivalent practical experience.