Principal Architect - AI & Full Stack - 10 - 15 Years - Bengaluru

About the role

Neuron7.ai is a rapidly growing AI-first SaaS company focused on creating a category-defining service intelligence product. Backed by top venture capitalists in Silicon Valley and a distinguished group of angel advisors/investors, we are recognized as a startup to watch. Our swift traction in the market underscores our mission to empower enterprises to make accurate service decisions at scale. Our AI-driven platform delivers service predictions in seconds by analyzing structured and unstructured data alongside insights from leading experts. In complex service environments-such as high-tech devices, manufacturing, and medical devices-Neuron7.ai helps service leaders excel in critical metrics like first-call resolution, turnaround time, and service margins. Learn more at Neuron7.ai. Why Join Us: At Neuron7.ai, you'll be part of a dynamic and innovative team that is redefining service intelligence. We value creativity, collaboration, and a commitment to pushing boundaries. As a Principal Architect / Senior Staff Engineer at Neuron7.ai, you will play a key leadership role in architecting, developing, and optimizing the backend infrastructure for our cutting-edge products. You will collaborate with cross-functional teams to design and deliver scalable, high-performance systems while mentoring and guiding other engineers. Role Overview Neuron7 is on a mission to redefine service excellence through Resolution Intelligence. We are seeking a Senior Staff Engineer for our Agentic AI team-a technical lighthouse and force multiplier who will architect the "Smart Resolution Hub" for the world's most complex service organizations. This is a role for a core builder who believes that "AI agents are software, not magic strings," and who has the engineering discipline to move beyond chatbots to create truly autonomous, goal-oriented systems. Primary Responsibilities Lead Agentic Architecture: Design and operate scalable multi-agent systems using planner-executor patterns and deterministic flow control. Architect Memory at Scale: Build the hierarchical memory stack (Ephemeral, Semantic, Structured) that ensures Neuro-our next-gen agent-can maintain context and learn from past outcomes across thousands of product lines. Data Intelligence Excellence: Lead the modeling of our Knowledge Graph infrastructure to unlock tribal knowledge and enable GraphRAG pipelines that reduce hallucinations to zero in mission-critical environments. Platform-as-a-Product: Build the foundational Agentic Platform-including tool registries, identity-resolved profile services, and standard A2A protocols-that enables teams across Neuron7 to deploy agents with high velocity. Inference Optimization: Take ownership of production readiness end-to-end, implementing intelligent model routing, semantic caching, and streaming to meet strict p95 latency targets (<2s). AgentOps Leadership: Establish architectural standards for continuous evaluation and trajectory analysis. Mentor our engineers on how to build for failure, implementing circuit breakers and human-in-the-loop escalation paths for high-stakes decisions. Continuous Adaptation: Lead our PEFT strategy, using LoRA-based fine-tuning to adapt open-source LLMs to niche service terminologies and deploying them efficiently via multi-adapter serving. Essential Technical Qualifications Education: Master's or PhD in Computer Science or a related technical discipline. Tenure and Impact: A minimum of 10-12 years of industry experience, with at least 3-5 years delivering production-grade AI/ML or Generative AI solutions at scale. System Mastery: Proven track record of architecting multi-agent orchestration systems (using LangGraph, AutoGen, or PydanticAI) and building complex, hierarchical memory stacks. Programming: High proficiency in Python and at least one compiled language (Java, Go, Rust, or C++) for performance-critical components. Data Intelligence: Expertise in Knowledge Graph modeling (Neo4j, Memgraph) and the construction of GraphRAG pipelines that outperform standard naive RAG in accuracy and explainability. Operational Discipline: Mastery of the full LLMops/Agentops lifecycle, including distributed tracing (OpenTelemetry), trajectory evaluation, and cost-per-goal optimization. Adaptation: Hands-on experience with parameter-efficient fine-tuning (LoRA, QLoRA) and multi-adapter serving frameworks. Behavioral Signals & Leadership System Thinking: You clarify the problem before proposing a solution, reasoning through trade-offs in structured versus unstructured knowledge retrieval. Platform Intuition: You understand what it takes to build a "Golden Path" for other developers, moving from ad-hoc scripts to reusable platform modules. Direction Setting: You don't just solve the ticket; you identify which problems are worth solving to move the company's EBITDA. Navigating Ambiguity: You thrive in the "fog," turning a vague requirement like "our diagnostic engine is slow" into a con

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