Responsibilities
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Build and maintain scalable backend services using modern technologies such as Python, Node.js, Java, or Go.
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Design and implement APIs that expose AI/LLM-powered capabilities to web, mobile, and enterprise applications.
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Integrate production-grade LLM APIs (OpenAI, Anthropic, Gemini, Azure OpenAI, etc.) into backend systems and workflows.
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Build and manage Retrieval-Augmented Generation (RAG) pipelines including ingestion, chunking, embedding, indexing, retrieval, reranking, and grounding.
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Design and maintain enterprise knowledge bases optimized for LLM and agent consumption.
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Build agentic workflows and multi-step reasoning systems using frameworks such as LangGraph, CrewAI, AutoGen, or equivalent.
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Design AI-enabled automation flows using tools such as n8n, Temporal, Airflow, or similar orchestration platforms.
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Build internal AI-powered developer productivity tools including code assistants, automated documentation generators, test generation systems, release-note generators, and incident-analysis agents.
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Handle LLM operational concerns including prompt management, context engineering, latency optimization, retries, fallback strategies, caching, observability, and cost optimization.
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Work with vector databases and search platforms such as Pinecone, Weaviate, Qdrant, Elasticsearch, or FAISS.
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Implement secure tool integrations and MCP (Model Context Protocol)-based workflows connecting APIs, databases, and enterprise systems to AI agents.
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Design monitoring and evaluation pipelines for AI systems including tracing, prompt/version tracking, hallucination analysis, token/cost monitoring, and performance evaluation.
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Collaborate closely with frontend, mobile, DevOps, QA, security, and product teams in a structured engineering environment.
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Contribute reusable SDKs, internal frameworks, and shared AI platform components.
Skills Required
Backend Engineering
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Strong backend development experience in Python, Node.js, Java, or Go
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REST / GraphQL API design and development
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SQL and NoSQL databases
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Distributed systems and asynchronous processing
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Queues and event-driven architectures (Kafka, RabbitMQ, Pub/Sub, etc.)
AI / LLM Engineering
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Production experience integrating OpenAI, Anthropic, Gemini, or equivalent LLM APIs
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Prompt engineering and context management
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RAG architecture and retrieval pipelines
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Knowledge base construction for LLM systems
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Embedding strategies:
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Dense embeddings
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Sparse retrieval
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Hybrid search
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Reranking pipelines
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Multilingual/domain-specific embeddings
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Vector databases and semantic search systems
Agentic AI & Workflow Orchestration
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LangGraph / CrewAI / AutoGen or similar agent frameworks
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n8n / Temporal / Airflow or equivalent orchestration systems
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MCP (Model Context Protocol) awareness and tool integration patterns
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Agent memory, tool-calling, and workflow design fundamentals
Observability & Reliability
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AI observability and tracing tools such as LangSmith, Langfuse, MLflow, OpenTelemetry, Grafana, Datadog, or equivalent
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Token, latency, retry, and cost monitoring
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Evaluation pipelines for prompts and agent workflows
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Logging, metrics, tracing, and production debugging
Cloud & DevOps
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AWS / GCP / Azure
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Docker and containerized deployments
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CI/CD pipelines
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Automated testing and release workflows
Ideal Profile
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4–7 years of backend engineering experience with strong system design fundamentals.
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Hands-on experience building AI-powered backend systems, RAG pipelines, or agentic workflows in production environments.
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Strong understanding of LLM limitations, hallucination mitigation, grounding strategies, and cost-performance tradeoffs.
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Experience designing scalable AI infrastructure and enterprise-grade APIs.
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Familiarity with agent monitoring, AI evaluation frameworks, and workflow orchestration platforms.
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Strong debugging, performance optimization, and problem-solving skills.
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Experience working in cross-functional product and engineering teams.
