AIOps Technical Associate

About This Role

Manage and monitor AI/ML operational workflows for model deployments, performance, and versioning across multi-cloud environments. Support AI agent and MCP server operations, track FinOps for cloud spend, and build dashboards for platform performance and model health.

Responsibilities

• Manage operational workflows for model deployments, updates, and versioning across GCP, Azure, and AWS
• Monitor model performance metrics including latency, throughput, error rates, token usage, and inference quality
• Track model drift, accuracy degradation, and performance anomalies, escalating to engineering as needed
• Maintain model inventory and documentation across multi-cloud environments
• Monitor AI agent health, performance, and reliability (AutoGen-based agents, MCP servers)
• Track and analyze AI/ML cloud spend across GCP (Vertex AI), Azure (OpenAI), and AWS (Bedrock)
• Build cost dashboards with breakdowns by model, application team, use case, and environment
• Build and maintain dashboards for platform performance, model health, agent metrics, and operational KPIs
• Support release management processes with pre/post-deployment validation checks
• Serve as operational point of contact for application teams consuming DxT AI APIs

Requirements

• 2-4 years in Ops/Technical Operations (MLOps, AIOps, DataOps, Platform Ops, or similar)
• Experience with AI/ML Concepts
• Experience with Cloud Cost Management and FinOps
• Proficiency with GCP, SQL, reporting tools (Looker, Tableau, Grafana, or similar)
• Basic Python for data analysis and scripting
• Experience with monitoring/observability tools (Datadog, Prometheus/Grafana, Cloud Monitoring, or similar)
• Understanding of APIs and API gateways (ability to read logs, trace requests, analyze traffic)
• Strong analytical and problem-solving skills
• Excellent communication skills

Qualifications

• College degree in Computer Science, BIS, MIS, EE, ME or similar
• 2-4 years in Ops, Analytics, or Technical Operations role (MLOps, AIOps, DataOps, Platform Ops, or similar)

Nice to Have

• Hands-on experience with LLM platforms: Vertex AI, Azure OpenAI, AWS Bedrock
• Familiarity with AI agents and agentic architectures (AutoGen, LangChain, or similar)
• Exposure to MCP (Model Context Protocol) or agent-tool integration patterns
• Experience with vector databases and RAG (Retrieval-Augmented Generation) operations
• Understanding of MLOps lifecycle
• Experience with APIGEE or similar API management platforms
• Familiarity with Responsible AI metrics
• FinOps certification or formal cloud cost management experience
• Experience supporting enterprise platform teams
• Familiarity with ML pipeline tools (Kubeflow, MLflow, Vertex AI Pipelines)
• Exposure to prompt management and evaluation frameworks
• ITIL or operational process framework experience
• Experience creating runbooks and operational documentation

Skills

* Required skills