You will work across the entire stack — from frontend experiences and APIs to distributed systems, data infrastructure, LLM orchestration, agentic workflows, evaluation systems, and autonomous optimization loops.
The ideal candidate has deep hands-on experience with Claude/Anthropic models, agentic architectures, tool-use, multi-step reasoning, autonomous execution loops, and AI-driven self-optimization.
You should be comfortable asking: "How can we make the system improve itself rather than requiring an engineer to manually optimize every workflow?"
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Full Stack EngineeringFrontend: React, Next.js, TypeScript, modern component architectures, state management, real-time and streaming AI interfaces, agent activity and execution interfaces, data visualization.Backend: Node.js, TypeScript, Python, REST APIs, GraphQL, WebSockets and streaming, event-driven architectures, background workers, job queues, distributed systems, authentication and authorization.
Distributed SystemsDesign systems that reliably execute thousands or millions of AI and data-processing tasks. Kubernetes, Docker, Cloud Run and serverless, message queues, Redis, Kafka or equivalent, distributed job processing, concurrency management, rate limiting, retries, idempotency, fault tolerance, observability. You know how to build systems that stay reliable when agents fail, APIs time out, models hallucinate, or downstream services go away.
Data & Learning InfrastructureBuild the infrastructure agents need to learn from historical executions. PostgreSQL, BigQuery or equivalent data warehouses, ClickHouse or analytical databases, vector databases, embeddings, retrieval systems, event logs, feature stores, analytics pipelines, data ingestion.
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Experience: 1+ year of Software Engineering / Backend Development experience
Job Description:
We are looking for a Software Engineer – Backend to join our engineering team and help build and operate secure backend services for an enterprise-search platform.
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You will work across the entire stack — from frontend experiences and APIs to distributed systems, data infrastructure, LLM orchestration, agentic workflows, evaluation systems, and autonomous optimization loops.
The ideal candidate has deep hands-on experience with Claude/Anthropic models, agentic architectures, tool-use, multi-step reasoning, autonomous execution loops, and AI-driven self-optimization.
You should be comfortable asking: "How can we make the system improve itself rather than requiring an engineer to manually optimize every workflow?"
...
Troubleshooting s Risk Mitigation: Identify and resolve technical bottlenecks during migration/deployment phases, ensuring minimal disruption to client operations.
Documentation s Handoff: Build standard operating procedures (SOPs), Project As-Builts, Low-Level Diagram(LLD), system architecture diagrams, and detailed handover documentation for ongoing support teams.
Process Optimization: Continuously refine deployment scripts, templates, and rollout checklists to improve implementation speed and consistency.
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