AI/ML Engineer: RAG & API Pipelines

We're looking for a Senior AI/ML Engineer to act as the bridge between data infrastructure and customer-facing AI products. You'll specialize in building low-latency API layers, production-grade RAG systems, complex ingestion pipelines, and Human-in-the-Loop workflows — working alongside Data Engineers to turn raw data lakes into live AI features.

Requirements

Must-Have Requirements Overall Experience: 7+ years in Backend Software Engineering and AI Application Engineering, including exposure to Distributed Systems AI & RAG Integration: 2+ years engineering production-grade RAG pipelines, managing vector retrieval context, and implementing secure validation layers for LLMs Prototyping & Collaboration: proven track record working synchronously with Data Engineers to rapidly turn raw data lakes and streams into production-ready AI feature prototypes Proficiency in Advanced API Design & GraphQL Architecture Proficiency in RAG, Data Flows & Ingestion Pipelines Proficiency in State Management & Human-in-the-Loop (HITL) Automation Production fluency in Python Working knowledge of C# (.

NET Core), Java, or Node.

js/TypeScript for enterprise ingestion systems Preferred Experience Token-aware pagination for GraphQL/REST endpoints (LLM context-safe) Custom Model Context Protocol (MCP) server development GraphQL schema implementation using Apollo Server or AWS AppSync Amazon Bedrock APIs (foundational model invocation and chaining) Knowledge Bases for Amazon Bedrock (chunking, metadata extraction, vector sync from S3) Hybrid retrieval using OpenSearch/Elasticsearch, pgvector, and MemoryDB / Redis OSS High-throughput ingestion workers for embedding and vector generation Ingestion pipelines with Amazon SQS, MSK (Kafka), and log sources Third-party SaaS analytics API integration (e.

g. , Pendo, Hotjar, Google Analytics) Autonomous Bedrock Agents with action groups Amazon Bedrock Guardrails (prompt injection blocking, PII redaction, safety alignment) Stateful orchestration with AWS St