GTM Engineer

Showpad; Pune District; On-site; INR 2,500,000 - 4,000,000; Full time

Job summary

Showpad is seeking a skilled GTM Engineer to design, build, and deploy AI-enabled workflows in a B2B SaaS context. The role emphasizes GTM processes, workflow automation, and cloud engineering with AWS, AI technologies, and scalable solutions.

The candidate will implement end-to-end GTM strategies, integrate multi-system workflows, and ensure the reliability, security, and observability of AI agents and orchestration. Collaboration with stakeholders is essential.

Qualifications

• 6+ years of GTM experience in a B2B SaaS or tech environment.
• Deep understanding of Lead-to-Revenue and customer lifecycle.
• Experience translating business problems to process and tech solutions.

Responsibilities

• Develop and implement end-to-end GTM strategies in a B2B SaaS setting.
• Create and optimize multi-system workflows and automations via REST APIs and event-driven patterns.
• Design and deploy serverless apps and event-driven architectures on AWS.
• Build and integrate AI agents and workflows using Amazon and Claude/Anthropic models.
• Collaborate with stakeholders to translate business problems into technology solutions.
• Ensure AI agents/workflows meet standards for reliability, scalability, and security.
• Contribute to standards for agent lifecycle management and observability.

Skills

GTM processes

Lead-to-Revenue

Workflow automation

AWS

API integration

Event-driven architecture

AI/ML concepts

Cross-functional collaboration

Tools

AWS Lambda

AWS Step Functions

Amazon Bedrock

API Gateway

CloudWatch

IAM

Secrets Manager

Job 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.
• Ability to quickly learn and evaluate emerging automation and data acquisition technologies.
• 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.