Job Title

Job Title: AI Integration EngineerLocation: Pune-BanerWork Mode: hybrid: 2 days from officeYears 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 ResponsibilitiesIntegrate 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 patternsManage prompt engineering, prompt versioning, and prompt evaluation frameworks Implement guardrails for LLM outputs: validation, content filtering, fallback logicMonitor AI system performance: latency, cost-per-query, accuracy drift, token usageCollaborate with frontend engineers to surface AI capabilities in product UIsOwn 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