Intermediate Backend Software Developer designing and building scalable backend infrastructure for AI features at Triton Digital. Collaborating with engineering teams to deliver efficient backend services and integrations.
Responsibilities
Design, build, and maintain scalable RESTful/GraphQL APIs and microservices that power our AI chatbot and other backend systems.
Implement agentic AI systems using frameworks like LangChain/LangGraph (or equivalent): multi-agent orchestration, tool calling to external APIs, reasoning loops, memory, and state management.
Integrate with third-party LLM providers and handle prompt engineering, rate limiting, cost optimization, and fallback logic.
Develop and optimize RAG pipelines — including document ingestion, embedding generation, vector search/retrieval, and context-aware response generation.
Work with both traditional databases and modern vector databases.
Build and maintain reliable integrations with external services via APIs, webhooks, and event-driven patterns.
Ensure high availability, performance, security, observability, and scalability of all backend services (monitoring, logging, caching, async processing).
Write clean, testable, well-documented code and actively participate in code reviews, architecture discussions, and agile ceremonies.
Collaborate closely with other engineering teams and product stakeholders to deliver end-to-end features.
Develop, configure, and maintain cloud infrastructure on which the applications run.
Participate in 24/7 on-call rotation for team-owned projects.
Requirements
5+ years of hands-on backend software development experience
Strong proficiency in Python and modern Python web frameworks (FastAPI, Django, or Flask strongly preferred)
Solid understanding of relational and NoSQL databases, ORM/query optimization, and data modeling
Experience designing and consuming RESTful APIs, GraphQL, or event-driven architectures (Kafka, RabbitMQ, etc.)
Familiarity with cloud platforms (AWS, GCP, or Azure) — especially serverless, containers (Docker), and basic orchestration (Kubernetes or similar)
Working knowledge of AI integration concepts : LLMs, embeddings, vector search, RAG patterns, and basic prompt/tool-calling techniques
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