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# Forward Deployed Engineer

- **Company:** RAKS Group LLC
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Big Data, Information Engineering, Data Infrastructure, Distributed Data Store, Distributed Systems, Memory Management, PostgreSQL, Reliability Engineering, SQL Databases, Unstructured Data, Large Language Models, Multi-Agent Systems, Backend, Data Layers, Low Latency, Machine Learning Operations
- **Published:** September 18, 2026
- **Apply:** https://www.dice.com/job-detail/8f35b243-db4a-4538-aaf9-c2a26df9ea03

## About the Role

* 5+ years building production systems, with genuine depth in AI/ML systems: how LLM-based systems, distributed data infrastructure, and agentic pipelines fail in production (drift, cost blowup, latency cliffs, brittle orchestration), and how to design around it.
 * Strong distributed systems and data engineering fundamentals: SQL/Postgres at a level where you reason about query plans and schema design under production constraints; comfort running these systems in production (monitoring, incident response, cost control).
 * Direct production experience with at least two or three of: agentic/orchestration systems, knowledge/data infrastructure at scale, model serving and inference, evaluation systems, access-control and authorization design.
 * Security-conscious by default.
 * Comfort taking an architecture with open decisions and closing the gaps yourself.

## Description

* Agentic and orchestration systems: multi-agent workflows, tool-use and agent-facing protocols (MCP or equivalent), state and memory management, tracing, replay, sandboxing.
 * Large-scale data and knowledge infrastructure: entity/relationship graphs, structured extraction from unstructured data using LLMs in bounded, evaluable ways, incremental pipelines that avoid full reprocessing on every change.
 * Model serving and inference: deployment, latency and cost optimization, reliability engineering for systems calling LLMs or client-hosted models at scale.
 * Evaluation and cost infrastructure: gold sets, regression detection, per-operation cost meters.
 * Access control and trust: permission and sensitivity enforcement built into the data layer, a single mutation/write gate every pipeline passes through.
 * Core backend and data engineering: schema design, query planning, distributed systems fundamentals.


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