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