Job Description

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.

Our Tech stack:

• (You don’t need to tick every box — we value curiosity, versatility, and a growth mindset more than deep mastery of a single stack.)
• Core backend & languages: Node.js, TypeScript, Fastify
• Databases & data handling: PostgreSQL, Redis , ElasticSearch
• Scalability & communication: PubSub, Webhooks, SSE
• AI-related: LLMs, LangGraph
• Infrastructure & deployment: Docker , GitHub Actions , Prometheus, Grafana
• Testing & reliability: Vitest, Supertest, Playwright