GTM Engineer / AI Product Builder

You will be working to design, deploy, and optimize Generative AI solutions that eliminate friction in our Go-To-Market (GTM) engine. You will move beyond traditional data analytics and modeling to build agentic workflows and RAG (Retrieval-Augmented Generation) systems, applying the latest advancements in Foundation Models to create intuitive, AI-native tools that supercharge our sales and marketing teams.

●     Building and refining LLM-powered features and autonomous agents to enhance our revenue capabilities.

●     Designing and implementing RAG pipelines to connect our proprietary data (e.g., playbooks, product documentation, past successful call transcripts) with large language models for high-accuracy, context-aware outputs.

●     Exploring and eliminating other GTM friction points by rapid prototyping AI solutions for lead enrichment, personalized outreach, and CRM automation.

●     Integrating AI workflows into the modern sales stack (e.g., Salesforce, Gong) to ensure seamless adoption by our sales reps.

●     Collaborating with Sales Leaders and BDRs to define how AI can best serve their workflow needs and improve interaction patterns with prospects.

●     Developing and maintaining production-grade infrastructure for AI orchestration, including prompt management, low-latency streaming APIs, and model evaluation frameworks.

●     Rapid "Vibe Coding" & Prototyping: Use tools like Claude Code, Codex, Lovable, LangSmith Agent Builder, to go from idea to deployed full-stack MVP in days, not weeks.

●     AI Evangelism & Enablement: Act as the "AI Copilot" for the revenue and GTM teams. Run internal workshops, create Gemini Gems and agent instruction files, and teach the broader org how to leverage AI tools to 10x their speed and conversion rates.

What we’re looking for...

You are a builder at heart with a "hacker" mindset and a passion for revenue operations. You are eager to bridge the gap between raw model capabilities and polished tools that actually help sales teams win. You are proactive, iterate quickly, and enjoy the challenge of "taming" non-deterministic AI outputs in high-stakes, real-time environments.

You’ll need to have:

●     Master’s degree or equivalent experience in Computer Science, Engineering, or a related technical field.

●     Strong proficiency in Python/TypeScript and experience building web services or integrating complex APIs (especially CRM, VoIP, or sales engagement platforms).

●     Solid understanding of the LLM stack, including experience with orchestration frameworks (e.g., LangChain/LangGraph, Strands Agents, CrewAI).

●     Knowledge of modern AI concepts: Prompt Engineering, few-shot learning, and the difference between fine-tuning and RAG.

●     Experience with standard software development tools (Git, Docker) and CI/CD processes.

●     A product-oriented and revenue-focused mindset: the ability to deeply empathize with a BDR's workflow and design AI outputs that are instantly actionable during a live call.

●     Evangelist Energy: You are genuinely excited about AI. You follow the latest releases, read the relevant papers, and can explain why "reasoning models" matter to a non-technical sales stakeholder.

●     Fearless "Hacker" Mindset: You aren't afraid of code you didn't write. You are comfortable generating 80% of your code with AI and crafting the remaining 20% with high precision.

Even better if you have one or more of the following:

●     Domain expertise in Go-To-Market, RevOps, or Sales tech stacks (Salesforce, Hubspot, Gong, Apollo, Clearbit).

●     Experience building real-time audio/speech-to-text processing pipelines (e.g., WebRTC, Deepgram, Whisper) for live transcription and analysis.

●     Contributions to open-source AI projects or a portfolio of "weekend" prototypes built with an AI-native toolchain

●     Exposure to cloud-based AI services (e.g., AWS Bedrock, Azure AI Studio, or GCP Vertex AI).

●     Experience with Vector Databases and semantic search concepts.

●     Experience with AI evaluation and observability tools

●     Knowledge of "agentic" design patterns

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