Backend Engineer- AI Integrations and Platforms

Job highlights

• Familiarity with vector databases (conceptual + practical). Caching strategies (Redis or equivalent). Good to Have . Python for AI tooling,agents,or batch workflows.
• Collaborate with frontend,analytics,and product teams. Technical Skills (Must Have) Backend & Architecture . Strong expertise in Node.js (Express / NestJS preferred). API-first design (REST

Node JS Python LLM integration Prompt orchestration Open AI APIs Gemini APIs Streaming responses Role Summary

Innovantes is building AI-enabled digital solutions for enterprise clients across automotive, healthcare, and analytics-driven domains. We are looking for a Backend Engineer with strong experience integrating and architecting AI services (LLMs, vision, speech, embeddings) into scalable production systems.

This role focuses on using AI services effectively, not building ML models from scratch.

Core Responsibilities

• Design and build scalable backend architectures using Node.js
• Develop secure, production-grade APIs that integrate AI services such as:
• LLMs (text generation, summarization, classification)
• Vision APIs (image analysis, OCR)
• Speech APIs (STT/TTS)
• Architect AI workflows, including:
• Prompt orchestration
• Tool/function calling
• Streaming responses
• Multi-step reasoning pipelines
• Build provider-agnostic abstractions (avoid lock-in to one AI vendor)
• Handle cost control, rate limits, retries, fallbacks, and caching
• Ensure data security and privacy, especially for enterprise clients
• Collaborate with frontend, analytics, and product teams

Technical Skills (Must Have) Backend & Architecture

• Strong expertise in Node.js (Express / NestJS preferred)
• API-first design (REST; GraphQL optional)
• Async processing, queues, background jobs
• Cloud-native architecture experience (GCP/AWS/Azure)

AI / LLM Integration (Critical)

• Hands-on experience integrating:
• Text generation
• Embeddings + semantic search
• Classification & extraction
• Deep understanding of:
• Token limits, context windows
• Prompt design vs system design
• Latency vs accuracy trade-offs
• Structured outputs (JSON schemas, tools/functions)

Data & Storage

• SQL (MySQL/Postgres)
• Familiarity with vector databases (conceptual + practical)
• Caching strategies (Redis or equivalent)

Good to Have

• Python for AI tooling, agents, or batch workflows
• Experience with RAG systems
• Experience building AI-powered enterprise tools (CRM, analytics, dashboards)
• Exposure to compliance-conscious environments (PII, data governance)