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# ML Engineer II

- **Company:** CYNET SYSTEMS INC.
- **Location:** Aliso Viejo, CA, United States
- **Experience:** Expert
- **Salary:** $108,160.0 - $118,560.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Amazon Web Services, Code Review, Continuous Integration, Information Engineering, Data Mining, Graph Database, Identity and Access Management, Information Retrieval, Python (Programming Language), Metadata, MongoDB, MySQL, Performance Tuning, OpenAI, Search Technologies, Service Development Studio, Systems Integration, Retrieval-Augmented Generation, Large Language Models, Indexer, Amazon Virtual Private Cloud (VPC), Backend, Fastapi, Pytest, AWS Aurora, Apache Kafka, Celery, Invoking Functions, Entity Resolution, Amazon Simple Queue Service (SQS), Docker
- **Published:** November 3, 2026
- **Apply:** https://www.thejobnetwork.com/job/9245850b-3282-4b2b-889d-395e64364391/ml-engineer-ii

## About the Role

* 10+ years of experience in end-to-end RAG architecture ownership (ingestion * retrieval * generation) & LLM orchestration.
 * 8+ years of strong hands-on Python backend/data engineering experience.
 * 6+ years of experience with OpenAI APIs (embeddings + chat + tools).
 * 6+ years of experience with MongoDB Atlas Vector Search (hands-on), Aurora MySQL + DocumentDB integration.
 * 6+ years of experience with API + async pipelines + CI/CD., * Strong experience with OpenAI APIs (embeddings + chat; tool/function calling patterns).
 * Hands-on with MongoDB Atlas Vector Search: Index creation/tuning, schema design, metadata filtering.
 * Strong integration experience with Aurora MySQL and DocumentDB.
 * API/service development: FastAPI preferred; background jobs (Celery/RQ/SQS/Kafka).
 * Engineering excellence: Docker, CI/CD, pytest, secure secrets, monitoring, performance tuning.

Qualification And Education:

 * Prior experience as Lead/Tech Lead owning architecture and delivery of production systems.
 * Proven experience shipping RAG/LLM applications in production.

## Description

* Own end-to-end architecture for ingestion, indexing, retrieval, and LLM orchestration (RAG).
 * Lead implementation in Python (hands-on coding + design reviews + standards).
 * Build and optimize data extraction pipelines from AWS Aurora MySQL and AWS DocumentDB.
 * Design chunking/metadata strategy and indexing into MongoDB Atlas Vector Search.
 * Integrate OpenAI for embeddings + generation.
 * Implement MCP patterns for safe retrieval and extensible tooling.
 * Establish production readiness: Observability, alerting, incident response runbooks.
 * Provide technical leadership: Mentor engineers, set coding standards, conduct design/code reviews.
 * Coordinate with product, data, and security stakeholders; manage technical roadmap.

Nice to Have:

 * Hybrid retrieval (keyword + vector), reranking, evaluation frameworks.
 * Document parsing/OCR, table extraction, knowledge graph/entity resolution.
 * AWS deployment experience (ECS/EKS/Lambda), VPC networking, IAM best practices.