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Mid-level, metro fullstack role with popular title but niche agentic-LLM requirements reduces applicant density.
Specialized agentic-LLM and knowledge-graph skills are transferable across AI-centric companies but less so to non-AI industries.
Multiple mandatory technical requirements (5+ years, Python backend, LLM production, vector DB, cloud) create strict filters.
Design, build, and own backend services and APIs (Python) that productionize AI/ML and agentic workflows with end-to-end responsibility including architecture, delivery, demos, and documentation.
Build and orchestrate multi-step agentic AI systems incorporating LLM workflows, tool calling, retrieval pipelines, and evaluation mechanisms.
Develop full-stack components including React-based front ends for internal tools, dashboards, and co-pilot interfaces as needed, collaborating directly with CTO office and pod team.
5+ years of backend engineering experience with Python frameworks (FastAPI/Flask/Django), including REST/gRPC APIs, async processing, and message queues.
Hands-on, production-level experience with LLM-based or agentic AI systems (e.g., LangChain, CrewAI, OpenAI SDKs) beyond tutorials, with shipping and debugging in production.
Experience with RAG pipelines including chunking/embedding, vector databases (pgvector, Pinecone, Qdrant, or similar), and retrieval quality evaluation.
On-site position in Hyderabad; work experience required: 5–9 years; cloud-native development experience on AWS or Azure with containerization and CI/CD.
Proven ability to own and deliver enterprise-grade AI/agentic system modules with product ownership—turning prototypes into reliable, scalable production services.
Experience working in pods or small teams collaborating closely with leadership and cross-functional roles including BA, Delivery Lead, and Architects.
Familiarity with advanced AI tech stacks including graph databases (Neo4j preferred), RAG architectures, and full-stack React/TypeScript development for internal tooling.