SeniorAIEngineer,AgenticSystems

## Accountabilities Design and implement multi-agent architectures with robust state management, memory systems, routing logic, and modular components such as planners, tool registries, and policy guards Evaluate and apply agentic frameworks such as LangGraph, LangChain Agents, AutoGen, CrewAI, LlamaIndex Agents, Semantic Kernel, or Haystack, including trade-off analysis Build and optimize retrieval-augmented generation (RAG) pipelines using vector databases and structured knowledge sources, ensuring grounding and relevance Integrate enterprise systems and data sources through APIs, function calling, event-driven architectures, and workflow orchestration tools Define and enforce reliability standards including SLIs/SLOs, observability, tracing, logging, and performance monitoring for agentic workflows Develop evaluation frameworks using automated and human-in-the-loop methods to measure accuracy, safety, cost, and task success Implement CI/CD pipelines, canary deployments, feature flags, and environment promotion strategies for production AI systems Enforce safety, privacy, and governance policies including data minimization, access control, redaction, and auditability Collaborate directly with enterprise clients to translate business processes into production-grade AI systems and validate solutions through proofs of value Requirements: 5–8+ years of experience in software engineering or platform engineering, with recent hands-on experience building production LLM or AI systems Strong experience with agentic frameworks such as LangGraph, LangChain Agents, AutoGen, CrewAI, or equivalent systems Deep understanding of RAG architectures including embedding strategies, vector databases, chunking, ranking, and grounding techniques Proven ability to build observable, secure, and cost-efficient AI systems with strong engineering discipline around testing and CI/CD Strong software engineering fundamentals in Python or TypeScript, including async programming, APIs, and distri