Senior Genai Engineer (Remote)

We are looking for aSenior GenAI Engineerto 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 You’ll DoArchitect 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 Experience5+ years of Python development with strong software engineering fundamentals.2+ years buildingproductionsystems powered by Large Language Models.Hands-on experience withLangGraphor similar agentic/orchestration frameworks (LangChain, etc.).Deep expertise withPydanticfor structured data modeling and validation.Strong understanding of LLM capabilities, limitations, prompt engineering, and evaluation methodologies.Production experience withasync Python(asyncio, concurrent request handling).Ability to design and implement complex system architectures with minimal guidance.Nice to HaveExperience withLiteLLMor multi-model abstraction layers.Familiarity with LLM observability tools (Langfuse, LangSmith).Background in vector databases andRAGpatterns.Understanding of cost optimization and token accounting.Experience withAzureand cloud-native architectures.Knowledge of evaluation frameworks and metrics for AI output quality.Technical ExpectationsArchitectural 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 OfferHighly competitive compensation, aligned with senior-level expertise and market benchmarks.Work with a global enterpriseleading innovation in AI-driven solutions - a truly international environment.Career growth planwith continuous learning, technical leadership opportunities, and exposure to cutting-edge AI projects.100% remote work, with flexibility, autonomy, and impact.

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.

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.