Principal AI Engineer

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BuzzBoard

WebsiteLinkedInAll Job Openings

51 - 200 employees

💼 Consulting

📣 Marketing

☁️ SaaS

Consulting • Marketing • SaaS

BuzzBoard is a B2SMB account intelligence platform that transforms the way marketing and sales teams engage with small to mid-sized businesses (SMBs). By applying data science and digital signals to an extensive collection of business intelligence, BuzzBoard uncovers data-driven insights that help drive meaningful conversations and increase sales throughout the customer lifecycle. The platform focuses not only on generating leads but also on addressing the reasons why prospects and customers will want to engage, offering capabilities for micro-targeting, upselling, and cross-selling, as well as automated monitoring of top accounts to enhance retention.

📋 Description

• Design AI systems behind content generation, business intelligence, recommendations, and agentic workflows • Create reusable prompts, evaluation flows, orchestration layers, and architecture patterns across products • Lead agent design covering reasoning, tool/API calls, state management, checkpointing, handoffs, and safe failure • Design end-to-end RAG pipelines, including chunking, embeddings, metadata, reranking, and retrieval evaluation • Own model selection based on cost, latency, accuracy, and reliability, including fallback and model-switching behavior • Build evaluation frameworks and regression testing for output quality, edit ratio, hallucination rate, schema adherence, latency, failure rate, and inference cost • Partner with engineering and platform teams on deployable, observable, and maintainable systems • Package services with Python, FastAPI/Flask, and Docker when appropriate • Diagnose AI-specific production issues such as rate limits, cost spikes, model failures, and degraded output • Mentor GenAI engineers and review designs, prompts, workflows, and evaluations • Translate product requirements into architecture with measurable acceptance criteria • Communicate technical tradeoffs to engineers and leadership

🎯 Requirements

• 5+ years in engineering, AI, ML, or data-product work • 3+ years of hands-on GenAI/LLM systems experience • Production GenAI experience with systems carrying real traffic • Deep working knowledge of LLMs and SLMs, prompt engineering, structured outputs, and tool/function calling • Hands-on experience with at least two major LLM ecosystems • Experience building production RAG with vector stores such as Chroma, Pinecone, Weaviate, or FAISS; embeddings, semantic search, and measured retrieval quality • Hands-on experience with at least one agentic framework such as LangGraph, CrewAI, AutoGen, or Semantic Kernel • Understanding of agent memory, state, tool integration, and failure handling • Strong Python skills • Comfort with REST APIs, Docker, a cloud platform, and basic CI/CD • Demonstrated evaluation work including regression testing, hallucination checks, schema validation, and output scoring • Ability to define metrics for quality, reliability, cost, and business impact • Track record guiding a small team or owning AI architecture end to end • Ability to create structure in a fast-moving environment with shifting requirements • Preferred: fine-tuning or supervised training workflows • Preferred: SLM and open-source model deployment using vLLM, Ollama, or TensorRT-LLM • Preferred: multimodal work across text, image, audio, or video • Preferred: Kubernetes or serverless deployment • Preferred: evaluation/observability tooling such as LangSmith, MLflow, or Weights & Biases • Preferred: marketing technology, SMB intelligence, or content-automation experience • Preferred: responsible AI, privacy, security, and compliance practice • Preferred: experience scaling high-volume AI systems

🏖️ Benefits

• Fully remote • A production GenAI foundation already carrying real volume • Genuine architectural ownership over the agentic systems that come next • A small, high-context team that moves quickly and argues about the work • Real impact on small businesses