P1C3STSEnd-to-end solution integration and technical qualityOwns the integrated Gen AI solution pipeline, stitching together modules built by AI Engineers into a coherent, functioning system across the full flowDesigns and implements end-to-end workflow orchestration using frameworks such as LangChain, LlamaIndex, LangGraph, AutoGen, CrewAI, or custom pipelines, ensuring correct data flow, state management, error handling, and fallback logic across all modulesOwns the agentic system design at an implementation level, including LLM routing, tool routing logic, context management, multi-agent coordination patterns, and guardrail integratio Manages the full prompt & context engineering lifecycle at system level, including prompt library architecture, versioning, templating, variable injection, and systematic evaluation of prompt variants across the integrated pipeImplements output reliability mechanisms across the pipeline, including structured output enforcement, JSON/schema validation, retry logic, self-consistency checks, and model output ground Identifies and resolves cross-module performance issues, including latency bottlenecks, inconsistent outputs across modules, cost overruns from inefficient model usage, or failures in multi-step reasoning chains Conducts integration testing and end-to-end system evaluation using Gen AI-specific metrics, including relevance, faithfulness, coherence, task completion rate, and tool-call accuracy in agentic flows, and frameworks such as RAGAS, architecture, versioning, templating, variable injection, and systematic evaluation of prompt variants across the integrated pipeli Implements output reliability mechanisms across the pipeline, including structured output enforcement, JSON/schema validation, retry logic, self-consistency checks, and model output groun Identifies and resolves cross-module performance issues, including latency bottlenecks, inconsistent outputs across modules, cost overruns from inefficient model usage, or failures in multi-step reasoning Conducts integration testing and end-to-end system evaluation using Gen AI-specific metrics, including relevance, faithfulness, coherence, task completion rate, and tool-call accuracy in agentic flows, and frameworks such as RAGAS, DeepEval, or custom eval harnesses T