AI 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 its about integrating orchestrating and deploying AI capabilities into production software reliably securely and at scale.

• 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

• 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/
• 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