Principal, Software Engineer-MLE, People. AI

About the role

We are looking for a Principal Software Engineer who is an architect-level expert in Python , with a deep mastery of LLM-driven systems, agentic architectures , and scalable intelligent automation. This is a pivotal role where you'll define the technical strategy , influence architecture across domains, and lead the creation of next-gen AI-driven platforms that move beyond prompt engineering into modular reasoning systems . This role isn't about building one-off workflows-it's about inventing and hardening intelligent systems that can reason, act, and adapt . You will shape the core architecture of multi-agent platforms , ensure LLM integrations are secure, efficient, and observable , and build frameworks that others can extend across use cases and orgs. As a Principal Engineer, you set the vision, write the critical path code, guide Staff and Senior engineers, and are the final word on whether a design is ready for scale.

Responsibilities

• Define and own the agentic architecture strategy across teams, including MasterAgent design, tool orchestration, memory layers, and dynamic router agents.
• Architect modular, testable, and composable Python systems that support multi-agent workflows, tool-chaining, RAG, memory management , and fallback strategies.
• Design LLM-powered execution engines that support both high throughput and adaptive reasoning (via LangChain, AutoGen, or custom frameworks).
• Lead implementation of retrieval-augmented generation (RAG) pipelines, semantic search , and structured knowledge memory systems.
• Build and scale integrations with internal LLMs , including handling signature-based auth, function calling , and context management at scale .
• Drive end-to-end lifecycle: from configuration schema (YAML) to execution trace logging , observability , and self-healing recovery patterns .
• Lead cross-org architecture reviews, influence roadmap prioritization, and set coding and design standards for all agentic platform work.
• Act as a multiplier by mentoring Staff/Senior engineers, building reusable libraries, and leading technical guilds around AI agent infrastructure.
• Must-Have Qualifications:
• 10+ years of professional software engineering experience, with 7+ years in Python , building distributed systems at scale.
• Deep knowledge of agentic design patterns , including:
• ReAct, Plan-and-Execute, AutoGen-style coordination
• Tool calling, dynamic agent routing, and recursive agent planning
• Semantic memory, embedding-based context lookup, summarization windows
• Expertise in building LLM-based systems with LangChain, OpenAI, Anthropic, or custom orchestrators.
• Hands-on experience with:
• RAG pipelines using vector stores (FAISS, Pinecone, Weaviate, Qdrant, Azure Cognitive Search)
• LLM evaluation and observability (tracing, token usage, agent state tracking)
• Workflow orchestration using config-first approaches (YAML/JSON definitions, step runners, etc.)
• Proven ability to drive technical vision, resolve ambiguity, and make architectural tradeoffs at scale.
• Strong background in distributed systems , task queues , asynchronous workflows , and backend performance optimization .
• Experience in cloud-native environments (AWS, GCP, or Azure), including containerization, monitoring, and secure API integrations.

Requirements

• Built or contributed to a custom agentic orchestration framework used across multiple product lines.
• Experience with vector search optimization , context ranking , or temporal memory solutions .
• Published talks, blogs, or papers on LLM systems, AI architecture, or applied reasoning frameworks.
• Deep understanding of how to apply LLM systems in regulated or high-compliance environments (PII handling, redaction, observability).
• Exposure to DevEx platforms for developers to build workflows on top of intelligent agents.
• Familiarity with multi-modal agents (text + vision), LLM simulation patterns, or offline evaluation loops.
• What Success Looks Like:
• You've built an agent platform that others across the org use as the foundation for intelligent automation.
• You turn abstract ideas into clean, extensible, production-grade Python systems that scale and evolve.
• You elevate technical conversations, coach Staff+ engineers, and are the go-to person for unblocking complex challenges.
• You are not just LLM-aware-you pioneer how LLMs are applied in production systems, with a clear perspective on what's reliable, efficient, and future-proof.
• Your influence goes beyond code-you shape tech culture, architectural direction, and platform vision .

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