Agentic AI Engineer - Cloud Infrastructure Automation

About Vericence

Vericence is a digital engineering and technology consulting firm helping enterprises build AI-driven platforms, modernize legacy systems, and scale innovation through cloud, data, and intelligent engineering. We partner with global organizations to deliver high-impact technology solutions and build world-class engineering teams.

Role Summary

We are seeking an Agentic AI Engineer to design and deliver AI agents that operate against live cloud infrastructure. This is a hands-on build role: you will stand up agent workflows that interpret infrastructure state and telemetry, generate and validate Infrastructure-as-Code, and execute controlled remediation and provisioning actions under human-in-the-loop approval.

This is not a research or prototyping role. The expectation is working, governed automation deployed into a real environment within the engagement window.

Key Responsibilities

• Build and deploy agentic workflows using an LLM orchestration framework (LangGraph, CrewAI, AutoGen, Semantic Kernel, or cloud-native equivalents such as Bedrock Agents / Azure AI Agent Service)
• Design and implement tool/function-calling layers that expose infrastructure operations to agents safely including MCP-based tooling where applicable
• Generate, review and validate Terraform modules programmatically; integrate policy-as-code checks (OPA/Sentinel, tflint, checkov) into agent output paths
• Integrate agents with observability and ITSM systems (DataDog, ServiceNow, PagerDuty, Jira) for alert triage and automated ticket-to-action flows
• Implement guardrails: least-privilege execution roles, approval gates, dry-run/plan-only modes, audit logging of every agent action
• Build evaluation and regression harnesses for agent behaviour (accuracy, tool-selection correctness, failure containment)
• Wire agent workflows into existing CI/CD pipelines
• Document architecture and hand over to the client's platform team at engagement close

Required Skills

Agentic AI / LLM

• 2+ years building production LLM applications, including at least one deployed multi-step agentic system
• Strong command of one orchestration framework (LangGraph/LangChain, CrewAI, AutoGen, Semantic Kernel) plus tool/function calling and structured output
• Practical experience with agent guardrails, retries, failure handling and human-in-the-loop approval design
• RAG fundamentals: embeddings, vector stores (pgvector, OpenSearch, Pinecone), retrieval quality tuning
• Agent evaluation and observability (LangSmith, Langfuse, Ragas, or equivalent custom harnesses)

Infrastructure / Platform

• 5+ years in cloud infrastructure, platform or DevOps engineering
• Deep Terraform: module design, state and remote backends, workspaces, drift handling; Terragrunt or Terraform Cloud/Enterprise a plus
• Policy-as-code: OPA/Rego, Sentinel, or equivalent
• Cloud depth in at least one of AWS / Azure / OCI, including IAM and least-privilege design
• CI/CD: GitHub Actions, Azure DevOps, GitLab CI or Jenkins
• Containers and orchestration (Docker, Kubernetes)
• Observability tooling - DataDog strongly preferred

Engineering

• Expert-level Python (async, API integration, testing)
• Secrets management (Vault, AWS Secrets Manager, Azure Key Vault)
• Git-based workflows and code review discipline

Nice to Have

• Prior experience building agents that take write actions against production infrastructure
• Model Context Protocol (MCP) server/client implementation
• FinOps tooling exposure (Cloudability, Apptio)
• Oracle Cloud Infrastructure (OCI)
• Healthcare or regulated-industry environment experience