AI/ML Engineer: RAG & API Pipelines
Jalasoft • Bolivia, Plurinational State of • Brazil
No Relocation
Posted: June 10, 2026
Job Description
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.
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 Hum...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 Step Functions or LangGraph
- Durable HITL gating workflows (pause, persist state, resume on human approval)
- Idempotent processing with automated state rollbacks and dead-letter queues (DLQ)
- End-to-end event tracing with OpenTelemetry (oTel) and Datadog
Additional Content
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.
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 Hum...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 Step Functions or LangGraph
- Durable HITL gating workflows (pause, persist state, resume on human approval)
- Idempotent processing with automated state rollbacks and dead-letter queues (DLQ)
- End-to-end event tracing with OpenTelemetry (oTel) and Datadog