# GTM Engineer at Greenhouse

- **Company:** Greenhouse
- **Location:** Pune +3
- **Employment type:** Full-time
- **Salary:** $130–150k
- **Category:** GTM Engineering
- **Seniority:** senior
- **Posted:** 2026-10-23
- **Stack:** n8n, Zapier
- **Page:** https://agentops.careers/jobs/gtm-engineer-greenhouse-4/
- **Apply:** https://job-boards.greenhouse.io/showpad/jobs/8319730

## In short (AI summary of the posting)

A GTM Engineer role at Greenhouse in Pune, building AI agents and AI-enabled workflows across go-to-market systems in a B2B SaaS setting. The work covers multi-system integration via REST APIs and event-driven patterns, serverless development on AWS, and agentic workflows using Amazon and Claude/Anthropic models. Requires 6+ years working with GTM processes and systems, plus hands-on AWS and automation experience.

## Description

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