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Competency Area
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Technical Expectations
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Agentic AI Architecture
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Understanding of AI agents, multi-agent workflows, MCP (Model Context Protocol), orchestration frameworks, tool calling, memory management, RAG patterns, and agent lifecycle management.
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AI/LLM Validation & Assurance
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Ability to define and implement testing strategies for AI systems including hallucination detection, response quality evaluation, guardrail testing, prompt validation, grounding verification, and reliability testing.
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QE Automation Engineering
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Strong hands-on experience with Playwright, Selenium, API automation, test frameworks, CI/CD integration, test data management, and automation architecture.
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Agent Development & Customization
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Ability to configure, extend, and customize agents for project-specific workflows, enterprise tools, APIs, business rules, and testing use cases.
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AI Observability & Monitoring
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Knowledge of agent telemetry, trace analysis, execution monitoring, prompt/response tracking, drift detection, performance analytics, and operational dashboards.
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Cloud & Platform Engineering
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Working knowledge of Azure AI, AWS Bedrock, OpenAI, Databricks, Kubernetes, containers, APIs, security integrations, and enterprise deployment patterns.
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Data & API Engineering
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Strong understanding of API contracts, service orchestration, structured/unstructured data, vector databases, embeddings, and data validation techniques.
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Responsible AI & Governance
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Experience validating security, privacy, compliance, explainability, bias detection, human-in-the-loop controls, and enterprise guardrails for AI agents.
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Performance & Scalability Testing
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Ability to validate agent response latency, concurrency, token consumption, workflow scalability, resiliency, and failover behavior.
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Solution Architecture & Consulting
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Capability to translate business use cases into agentic solutions, define reusable marketplace assets, establish standards, and mentor engineering teams.
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