AI Ops Engineer
Remote, United States
R
USD 160K-220K (estimate) Mid-level Full Time
Tasks
- Apply guardrails validation access controls and human in the loop patterns
- Build Model Context Protocol integrations and tool calling
- Build incident triage and root cause analysis AI workflows
- Collaborate with cross-functional teams to deliver production solutions
- Design LLM powered agentic solutions for I O use cases
- Develop and integrate AI agents for operational workflows
- Develop backend services using Python and REST APIs
- Extend workflows to controlled automation with change window support
- Implement AI observability and evaluation metrics
- Implement Retrieval Augmented Generation and grounding strategies
- Ingest normalize enrich and contextualize operational data for AI
- Integrate AI platform with ServiceNow CMDB Dynatrace Zabbix and GCP
- Reduce LLM hallucinations using knowledge quality and cross validation
Perks/Benefits
- 401k match
- Career coaching
- Employee assistance programme
- Employer-paid life insurance
- Flexible working hours
- Hybrid work
- Training opportunities
- Wellbeing programme
Skills/Tech-stack
Access Control | Agent systems | Agentic AI | Autogen | CMDB | Cloud platform | CrewAI | Data Governance | Data Security | Dynatrace | Embeddings | Function Calling | GCP | Generative AI | Google Cloud | Google Cloud Platform | Human-in-the-loop | ITSM | Incident Management | Infrastructure Telemetry | Langchain | Langgraph | Language Models | Large Language Models | MTTR | Model Context Protocol | Monitoring | Multi-Agent | Multi-Agent Systems | Observability | Prompt engineering | Python | RAG | REST API | Retrieval-Augmented Generation | ServiceNow | The Loop | Token Management | Tool-Calling | Vector Databases | Zabbix
Education
N/A
Roles
AI | AI & Automation Engineer | AI Automation | AI Ops Engineer | Automation Engineer | Engineer | Ops Engineer
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