Job Description

AI Architect/developer

Summary

Build, integrate and scale enterprise AIOps, automation and Agentic AI/GenAI-enabled operations capabilities. The role converts operational pain points into governed use cases for event intelligence, knowledge-assisted triage, incident summarization, predictive insights and closed-loop remediation across hybrid infrastructure, network, cloud and application environments.

Responsibilities

Design and implement Agentic AI, AI-assisted event correlation, incident classification, RCA, knowledge/RAG, summarization and recommended-action capabilities for POCs.

Prepare prompts and AI instructions, define confidence and evidence handling, capture analyst acceptance or override, and tune outputs against test results.

Implement AI guardrails covering access, sensitive data, explainability, auditability, quality and human approval checkpoints.

Define expected AI outcomes with domain SMEs and evaluate responses against realistic happy-path, exception and failure scenarios.

Integrate AI services with events, topology, knowledge, workflows and ITSM interfaces, using mocks where source systems are unavailable.

Develop automation workflows for diagnostics, ticket creation, enrichment, notification, remediation, validation and rollback using APIs, scripts and orchestration platforms.

Implement Agentic AI/GenAI-assisted capabilities such as incident summarization, knowledge retrieval, recommended actions and operator copilots with defined human approval checkpoints.

Establish guardrails for prompt handling, sensitive data, access control, audit logging, model quality, hallucination risk and responsible use in operations.

Integrate automation platforms with ITSM, CMDB, collaboration, knowledge, network and security systems using REST APIs, webhooks and event-driven patterns.

Troubleshoot workflow and model-response issues across development, test and production environments.

Create reusable patterns, technical standards and implementation accelerators, and mentor team members according to assigned seniority.

Primary Skills: GenAI assistants and agents development, RAG, prompt framework & governance, model evaluation, Kubernetes, AIOps, automation, APIs, Python or PowerShell, ITSM integration, data analytics and production implementation, AWS cloud platforms, proposal development, effort estimation/costing and executive presentations. Practical knowledge and hands-on experience with any Enterprise Network technologies such as SDWAN, WiFi, MPLS, Data Centers, Campus (LAN/WAN).

Secondary Skills:  SQL and log analytics, ITSM lifecycle, Enterprise observability, event management.

Required Experience and Qualifications

Relevant experience in AIOps, automation, platform engineering, data engineering or AI-enabled operations.

Experience with scripting and API integration, including authentication, payload design, error handling, rate limits and operational logging.

Practical knowledge of AI/GenAI operational patterns, with the ability to distinguish deterministic automation from probabilistic AI recommendations.

Ability to produce solution designs, implementation plans, runbooks, test evidence and production handover documentation.

Preferred / Nice-to-Have Capabilities

Experience in telecom, enterprise network platform or managed-service delivery environments.

Exposure to agentic workflows, MCP-compatible tools, RAG, vector search, model gateways or AI observability.

Cloud and container experience across Azure, AWS  and Kubernetes-based platforms.

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