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.
