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.

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