AI Integration Engineer

Sri Lanka·Colombomid

OtherAi Integration Engineer

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Quick Summary

Key Responsibilities

document ingestion, chunking strategy, vector store selection, retrieval tuning Build agentic workflows using frameworks such as AgentCore, LangChain, LlamaIndex,

Requirements Summary

vector databases (Pinecone, Weaviate, pgvector, etc.), embedding models, chunking strategies Understanding of prompt engineering: system prompts, few-shot examples, chain-of-thought,

Technical Tools

OtherAi Integration Engineer

The AI Integration Engineer builds the connective tissue between LLM APIs, existing product systems, and end users. This is not a model training role — it's about integrating, orchestrating, and deploying AI capabilities into production software, reliably, securely, and at scale.

Responsibilities

~1 min read

→Integrate LLM APIs (Anthropic Claude, OpenAI GPT-4o, 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 across the SDLC, from spec through CI/CD to production observability

Requirements

~1 min read

3+ years backend or full-stack 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, and cost management for LLM-based services
Experience with AWS or another major cloud platform

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 personal development workflow