Senior Backend Engineer with AI

Job Description

You’ll join AI Engines – the team building the backend “brain” of AI Agents in Text App.

AI Agents are intelligent, task-performing assistants powered by our Text Intelligence platform. They understand context, communicate naturally, and can take meaningful actions: from answering customer questions using the Knowledge Hub, to guiding human agents, collecting data, or automating tasks in third-party systems. In chats, they appear as chatbots; in tools like Copilot they power smart suggestions and actions.

Our team makes this possible by designing scalable backend systems that will be able to handle millions of conversations daily, building APIs and services for agent management, developing the reasoning layer that drives agent decisions, and continuously improving knowledge retrieval and agent skills.

You will be responsible for:

Building and maintaining the core backend architecture (Vector Database, AI Engines, Knowledge Hub, etc.).

Designing and optimizing scalable systems capable of handling very high traffic and real-time data flows especially for high-volume chat and data ingestion.

Migrating implementations from legacy systems to a modern ecosystem.

Collaborating with AI researchers to experiment, validate, and deploy new AI features into production.

Conducting R&D and experiments with AI Agents ecosystems, including prompt management and reasoning approaches.

Designing and extending RESTful APIs and service integrations for AI and data-heavy use cases.

AI Agent integrations with MCP servers, shaping how agents connect and operate across systems.

Participating in code reviews, architecture discussions, and technical decision-making, aligning backend engineering with the overall AI strategy.

You may be a good fit for this role if you:

Have 6+ years of experience in backend development, building scalable and efficient systems.

Work fluently with Node.js and TypeScript, including queues, streams, and async programming.

Have hands-on experience with real-time communication (WebSockets, async service-to-service messaging).

Have experience in commercial work on artificial intelligence project and understand AI and LLM integration, especially concepts like RAG, agentic systems, semantic search, and vector databases.

Understand SQL databases (e.g., Postgres) and know how to design, optimize, and maintain them in data-intensive environments.

Bring experience with NoSQL databases, especially Elasticsearch

Know how to run and scale services on Google Cloud Platform and Kubernetes, including serverless computing and data pipelines.

Enjoy solving complex technical challenges in microservices, high-traffic systems, concurrency, and performance optimization.

Bonus if you also orked on SaaS products or AI-driven platforms. and have experience with workflow automation or low-code/no-code tools.