Multi-Agent Marketing Orchestrator

As a Multi-Agent Marketing Orchestrator at Cehpoint, you will architect and deploy autonomous multi-agent systems that plan, execute, and continuously optimize marketing campaigns end-to-end. Your work sits at the intersection of AI engineering and growth marketing, turning agent swarms into scalable, data-driven revenue engines for enterprise clients.

• Design and implement multi-agent architectures where specialized agents (content, SEO, paid-media, analytics, CRM) collaborate autonomously to plan and execute marketing campaigns.
• Build agent orchestration layers using frameworks such as LangGraph, CrewAI, AutoGen, or custom MCP-based pipelines with tool-use, memory, and handoff protocols.
• Develop real-time campaign feedback loops that ingest engagement metrics, attribution data, and conversion signals to trigger agent re-planning and budget reallocation.
• Integrate agent systems with marketing platforms (HubSpot, Salesforce, Meta Ads, Google Ads, Mailchimp, GA4) via APIs and webhooks for seamless execution.
• Implement guardrails, content approval gates, and policy enforcement checks to ensure agent outputs comply with brand guidelines and regulatory requirements.
• Monitor agent performance, cost-per-call, latency, and output quality; set up observability dashboards and alerting for production agent fleets.

• Foundations of multi-agent systems: goal decomposition, task allocation, consensus mechanisms, and agent communication protocols (AASM, BDI models).
• LLM reasoning paradigms including ReAct, Tree-of-Thoughts, plan-execute-reflect loops, and function/tool calling for agentic behavior.
• Marketing funnel mechanics and attribution modeling: understanding how top-funnel awareness, mid-funnel consideration, and bottom-funnel conversion metrics inform agent decision-making.

• Python with LangChain, LangGraph, CrewAI, or AutoGen for building and orchestrating agentic workflows.
• REST API integration and webhook-based event handling across marketing SaaS platforms (HubSpot, Salesforce, Zapier, Make).
• SQL and data-query skills for building campaign analytics pipelines from GA4, BigQuery, or PostgreSQL sources.
• Cloud deployment using AWS (Lambda, ECS, Bedrock), GCP (Vertex AI, Cloud Functions), or Azure (Function Apps, Azure AI) with infrastructure-as-code (Terraform/CDK).
• Observability tooling: LangSmith, Arize, Prometheus/Grafana, or ELK Stack for logging, tracing, and monitoring agent behavior in production.

• Fascinated by how autonomous systems can mimic and outperform human marketing teams — constantly experimenting with novel agent collaboration patterns beyond textbook designs.
• Obsessively tracks the latest research on LLM reasoning, tool use, and agentic architectures, and can articulate how each breakthrough translates into a practical marketing automation advantage.
• Naturally questions assumptions about campaign performance — treats every agent output as a hypothesis to validate through A/B tests and real-world conversion data.

• Competitive compensation (₹12L–₹20L/year) with performance-linked bonuses and early-stage equity considerations.
• Fully remote-first setup with a yearly home-office stipend (₹50,000) and annual team offsite.
• Generous GPU/cloud credits for personal projects, experimentation, and open-source contributions.
• Direct mentorship from senior AI engineers and security architects; structured learning budget for conferences and certifications.
• Impact-driven culture: your agent systems ship to live enterprise clients within weeks, not quarters.