Applied AI Engineer (AI Agents & Workflow Automation)

We help creators get more out of every conversation with Instagram-focused automations and support for other channels like Messenger, WhatsApp, and TikTok. The result? Better engagement, more sales, and real, sustainable growth.

With a diverse team of 350+ people spread across three continents, we’re building the leading Chat Marketing platform that is used — and loved — by more than 1.5 million customers worldwide.

The AI era isn't coming, it's already here. At Manychat, we're building it into the foundation of everything  and we need an Applied AI Engineer to help make that real.

Your main focus will be AI agents and agentic workflows, LLM-powered system orchestration, and automation pipelines that integrate into the business processes that actually matter. You'll work across internal and customer-facing systems, writing production-grade code and partnering with product and business teams to turn complex challenges into reliable, scalable solutions.

We'd love to work with someone who brings more than strong engineering fundamentals. Someone with the product mindset to understand business context, the communication skills to work fluidly across teams, and the pragmatism to choose solutions that are effective and built to last. Ownership matters here: we're looking for someone who drives work from discovery to rollout, not someone who hands things off at the first checkpoint.

If success for you means identifying real AI use cases, building things that work reliably at scale, and being a trusted technical partner to the teams around you, let’s talk! 

• →Design, build, and maintain AI-driven services and agent-based solutions for workflow and business process automation.
• →Develop orchestration logic across LLMs, internal services, APIs, databases, and third-party platforms.
• →Build backend components for AI products and automation pipelines using TypeScript and Node.js.
• →Integrate AI-powered workflows with CRM, ERP, ticketing, knowledge management, communication, and other business systems.
• →Implement and improve RAG, tool/function calling, memory/state handling, evaluation, and monitoring for AI use cases.
• →Partner closely with Product Managers, domain experts, and business stakeholders to identify high-impact use cases and translate them into technical solutions.
• →Contribute across the full product lifecycle: discovery, prototyping, production delivery, and iteration.
• →Ensure production quality through testing, observability, reliability, security, performance, and cost awareness.
• →Document architecture, technical decisions, and tradeoffs, and communicate them clearly to both technical and non-technical stakeholders.

• 3 – 5 years of experience in software engineering, backend engineering, or applied AI engineering.
• Strong hands-on experience with TypeScript and Node.js in production environments.
• Experience building backend services, APIs, and integrations.
• Practical experience with LLM-based applications, AI automation, or agentic workflows.
• Solid understanding of RAG, prompt engineering, tool/function calling, and context management.
• Experience working with SQL/NoSQL databases and external APIs.
• Understanding of reliable backend patterns such as async processing, retries, queues, idempotency, and error handling.
• Ability to understand business workflows and translate ambiguous requirements into clear technical solutions.
• Experience collaborating with product teams, analysts, operations, and other stakeholders.
• Strong communication skills and the ability to explain complex solutions to both technical and non-technical audiences.

• Experience with LangChain, LangGraph, OpenAI SDK, LlamaIndex, or similar frameworks.
• Experience with workflow and orchestration tools such as n8n, Temporal, or event-driven systems.
• Familiarity with PostgreSQL, Redis, Kafka, BullMQ, or similar technologies.
• Experience with vector databases such as pgvector, Pinecone, Qdrant, or Weaviate.
• Experience with Python in AI/ML-related use cases.
• Experience with Docker, cloud platforms (AWS, GCP, Azure), and CI/CD pipelines.
• Exposure to observability and evaluation tooling for AI systems.
• Understanding of security, privacy, and governance considerations for AI-driven business systems.

• Core: TypeScript, Node.js
• Backend: NestJS, Express, or similar frameworks
• AI/LLM: OpenAI API, Anthropic API, LangChain, LangGraph
• Data/RAG: PostgreSQL, pgvector, Redis, vector databases
• Integrations: REST APIs, webhooks, third-party SaaS platforms

Infrastructure: Docker, cloud services, CI/CD, logging and monitoring