Forward Deployed Engineer – AI Solutions Architect

Embrace Software Inc

Forward Deployed Engineer developing production RAG systems for enterprise customers in remote work environment. Leading teams and embedding with Fortune 500 client engineering teams.

Posted 5/27/2026full-timeRemote • 🇺🇸 United StatesSeniorLeadWebsite

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformPython

About the role

Key responsibilities & impact

Design and ship production RAG systems for enterprise customers — chunking strategy, embeddings, hybrid retrieval, reranking, query rewriting, citation grounding, the works.
Own retrieval evaluation end-to-end — define golden sets, build eval pipelines (Ragas, TruLens, LangSmith, or custom), and drive measurable quality wins (think context precision 0.65 → 0.90, not "looks good to me").
Architect agentic systems layered on top of RAG — multi-step retrieval, tool use, MCP integrations, guardrails, output validation.
Embed directly with customer engineering teams — pair on whiteboards, write code in their repos, debug their data, present to their CTO.
Technically lead a distributed team of FDEs across time zones — set the quality bar, review architecture, mentor engineers, own customer outcomes.
Close the loop with our product org — what breaks in the field shapes what we build next.

Requirements

What you’ll need

7+ years engineering experience, with at least 2 years shipping production RAG (not POCs) at real scale.
Demonstrated depth on the parts of RAG that actually matter: Chunking strategies and the ablations you ran to choose one.
Vector DB trade-offs from real production use — Pinecone, Qdrant, Weaviate, pgvector, Vespa, OpenSearch (pick your 2–3 and have opinions).
Hybrid search, reranking (Cohere, cross-encoders, ColBERT), HyDE, query rewriting, context compression.
Retrieval and generation evaluation — you can quote real numbers from real systems you've owned.
The boring-but-critical stuff: access control, citation enforcement, freshness, multi-tenancy, cost-at-scale.
Strong Python; production experience on at least one cloud (AWS / Azure / GCP).
Hands-on with at least one agent framework — LangGraph, AutoGen, CrewAI, Semantic Kernel, or equivalent.
Customer-facing presence — you can walk into a Fortune 500 architecture review and earn trust in 30 minutes.
Track record of leading engineers, not just being one.

Benefits

Comp & perks

Competitive salary commensurate with experience.
Opportunities for career advancement and professional development.
Experience collaborating with a diverse, global team within a remote work setting.

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

RAG systemschunking strategyembeddingshybrid retrievalrerankingquery rewritingcitation groundingPythonvector databasesretrieval evaluation

Soft Skills

leadershipmentoringcustomer-facing presencecommunicationcollaboration