AI Integration Engineer(LLM, API ,RAG, Python)-US based IT Co.-Pune (hybrid)

Job Title: AI Integration Engineer

Location: Pune-Baner

Work Mode: hybrid: 2 days from office

Years of experience: 3+yrs (backend or full stack engineering)

Purpose:

The AI Integration Engineer builds the connective tissue between LLM APIs, existing product systems, and end users. They are not training models. They are integrating, orchestrating, and deploying AI capabilities into production software, reliably, securely, and at scale.

Key Responsibilities

• Integrate LLM APIs Anthropic Claude, OpenAI GPT4o, AWS Bedrock, Google
• Gemini) into backend services and user-facing products.
• Design and implement RAG pipelines: document ingestion, chunking strategy,
• vector store selection, retrieval tuning.
• Build agentic workflows using frameworks such as AgentCore, LangChain,
• LlamaIndex, or custom orchestration patterns
• Manage prompt engineering, prompt versioning, and prompt evaluation frameworks
• Implement guardrails for LLM outputs: validation, content filtering, fallback logic
• Monitor AI system performance: latency, cost-per-query, accuracy drift, token
• usage
• Collaborate with frontend engineers to surface AI capabilities in product UIs
• Own the AI integration layer in the SDLC, from spec to CI/CD to production
• observability.

Skills & Qualifications:

Must-Have (Core Competencies)

3 years backend or fullstack experience; strong API design and consumption

skills

Proven experience integrating LLM APIs (any major provider) into production

applications, not just prototypes

Proficiency in Python and/or TypeScript/Node.js

Hands-on experience with RAG vector databases Pinecone, Weaviate,

pgvector etc), embedding models, chunking strategies.

Understanding of prompt engineering: system prompts, few-shot examples,

chain-of-thought, structured output

Solid grasp of API security, rate limiting, cost management for LLM-based

services

Experience with AWS or another major cloud platform.

Desirable:

Experience with agentic frameworks: AgentCore, LangChain, LlamaIndex,

CrewAI, AutoGen, or similar

Familiarity with multi-modal AI (vision, audio) or function calling / tool use

Understanding of fine-tuning workflows even if not hands-on

Experience using AI coding assistants to accelerate their own development

workflow