GTM Engineer

Summary: We are seeking a skilled GTM Engineer to join our dynamic team. This role plays a critical part in designing, building, and deploying AI agents and AI-enabled workflows, leveraging advanced technologies to drive business outcomes in a B2B SaaS environment. The ideal candidate will have a strong background in GTM processes, workflow automation, AWS, cloud engineering, and AI technologies. Responsibilities: - Develop and implement end-to-end GTM strategies and processes within a B2B SaaS environment. - Create and optimize multi-system workflows and automations using REST APIs and event-driven integration patterns. - Design and deploy serverless applications and event-driven architectures using AWS services. - Build and integrate AI agents and workflows using Amazon, Claude/Anthropic models, and approved business systems. - Collaborate with stakeholders to translate business problems into effective technology solutions. - Ensure AI agents and workflows meet standards for accuracy, reliability, scalability, and security. - Contribute to the development of standards for AI agent lifecycle management and observability. Required Skills: GTM & Business Experience - 6+ years of experience working directly with GTM processes and systems in a B2B SaaS or technology environment. - Strong understanding of end-to-end Lead-to-Revenue and customer lifecycle processes. - Demonstrated understanding of Sales, Marketing, Customer Success, and Revenue Operations workflows. - Strong understanding of core SaaS commercial concepts and metrics. - Experience translating business problems into process and technology solutions. - Demonstrated ability to work directly with business stakeholders and challenge or refine requirements. - Experience working across multiple GTM platforms and understanding cross-system dependencies. Workflow Automation & Integration - Strong understanding of REST APIs, webhooks, JSON, OAuth, authentication, and event-driven integration patterns. - Experience designing multi-system workflows and automations. - Experience with workflow orchestration technologies. - Ability to evaluate when low-code/no-code automation is appropriate versus custom development. - Experience with platforms such as Zapier, n8n, Workato, Make, Tray.io, or comparable technologies is beneficial but not required. - Experience with web scraping, browser automation, data extraction, or related technologies is beneficial. - Ability to quickly learn and evaluate emerging automation and data acquisition technologies. AWS & Cloud Engineering - Hands-on experience with AWS Lambda and serverless application patterns. - Experience with AWS Step Functions or comparable workflow orchestration concepts. - Experience with Amazon EventBridge and event-driven architecture. - Experience with Amazon Bedrock or demonstrated experience building applications using foundation-model APIs. - Familiarity with API Gateway, CloudWatch, IAM, Secrets Manager, S3, queues, databases, and related AWS services. - Understanding of cloud security, permissions, monitoring, and production support practices. AI Agents & Agentic Orchestration - Design, build, and deploy AI agents and AI-enabled workflows within the approved architecture. - Use Amazon and Claude / Anthropic models for reasoning, classification, summarization, research, recommendations, and decision support. - Build agentic workflows combining LLM reasoning with structured business data, APIs, deterministic rules, web-derived information, and approved tools. - Integrate agents with approved business systems and data sources through available APIs, connectors, and Model Context Protocol (MCP) capabilities. - Understand how to discover, configure, and use available MCP servers and tools within AI workflows. - Evaluate when an existing MCP capability can simplify access to a system or data source rather than building a custom integration. - Design prompts, context, structured outputs, tool/function calling, guardrails, and human-in-the-loop patterns. - Determine when deterministic workflow logic should be used instead of an LLM or agent. - Test agents and workflows for accuracy, reliability, security, latency, scalability, cost, and measurable business value. - Contribute to standards for agent permissions, observability, evaluation, and lifecycle management. AI & LLM Technologies Candidates should have hands-on experience with or strong practical knowledge of: - Claude / Anthropic and/or other leading foundation models - Prompt and context engineering - Structured outputs - Tool / function calling - Agentic workflows and AI orchestration - Model Context Protocol (MCP) concepts and use of MCP-enabled tools - Connecting AI workflows to approved systems and data sources - Retrieval-Augmented Generation (RAG) concepts - Human-in-the-loop patterns - Model and agent evaluation - AI observability - AI security, guardrails, data privacy, and responsible AI Prior MCP experience is beneficial but not required. Candidates should be able to quickly learn and leverage MCP-enabled capabilities as part of AI and automation workflows.