Architect GenAI Workflows Design end-to

We are looking for a Senior GenAI Engineer to design and build advanced generative AI workflows that power our next-generation coverage analysis platform. You will architect complex multi-step agentic systems using modern orchestration frameworks transforming ambiguous business challenges into scalable production-grade AI solutions. Operating at the intersection of system design and AI innovation you will drive the technical evolution of how our platform leverages large language models. What Youll Do Architect GenAI Workflows Design end-to-end agentic systems using LangGraph structuring complex problems into modular composable steps and nodes. Build Orchestration Layers Implement robust LLM orchestration with LiteLLM or similar tools managing multi-model strategies fallbacks routing and cost efficiency. Data Structuring Validation Create reliable typed data flows using Pydantic models across pipelines APIs and internal services. Model Strategy Integration Evaluate and integrate multiple LLM providers (OpenAI Claude etc.) optimizing for latency cost and output quality. Observability Debugging Implement logging tracing and monitoring for AI systems using tools such as Langfuse or LangSmith. Performance Optimization Improve prompt engineering token usage chunking strategies and inference efficiency across the platform. Cross-functional Collaboration Work closely with data scientists AI engineers and product teams to refine prompts deploy systems and shape new features. Must-Have Experience 5+ years of Python development with strong software engineering fundamentals. 2+ years building production systems powered by Large Language Models. Hands-on experience with LangGraph or similar agentic/orchestration frameworks (LangChain etc.). Deep expertise with Pydantic for structured data modeling and validation. Strong understanding of LLM capabilities limitations prompt engineering and evaluation methodologies. Production experience with async Python (asyncio concurrent request handling). Ability to design and implement complex system architectures with minimal guidance. Nice to Have Experience with LiteLLM or multi-model abstraction layers. Familiarity with LLM observability tools (Langfuse LangSmith). Background in vector databases and RAG patterns. Understanding of cost optimization and token accounting. Experience with Azure and cloud-native architectures. Knowledge of evaluation frameworks and metrics for AI output quality. Technical Expectations Architectural thinking Ability to decompose ambiguous AI problems into clean modular components. Production mindset Focus on building robust observable maintainable systems rather than prototypes. LLM fluency Deep practical knowledge of working effectively with large language models. Systems perspective Understanding of how AI components interact with APIs databases async workers and monitoring layers. Initiative Ownership Comfortable driving technical direction and owning end-to-end design decisions. What We Offer

Highly competitive compensation aligned with senior-level expertise and market benchmarks. Work with a global enterprise leading innovation in AI-driven solutions a truly international environment. Career growth plan with continuous learning technical leadership opportunities and exposure to cutting-edge AI projects. 100 remote work with flexibility autonomy and impact.