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 Servicesensuring reliability, security, performance, cost-efficiency, and governance of customer AI workloadswhile 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
- 35 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.
