Senior AI Systems Engineer (RAG)

Jobot; South Salt Lake (UT); Remote; USD 170,000 - 200,000; Full time

Benefits offered by this job

Healthcare benefits

Equity

Remote work

Job summary

Jobot is hiring a Senior AI Systems Engineer focused on RAG, information retrieval, and LLM context engineering. You will own systems for identifying relevant information, retrieving it efficiently, and constructing high-quality context for language models.

We value autonomy, equity, and strong engineering skills in a well-funded, fully remote environment. Join a senior team and help scale platform services with excellent benefits.

Qualifications

• Experience building and operating production AI systems.
• Strong expertise in RAG and information retrieval.
• Familiar with semantic and vector search, embedding strategies, and context management.

Responsibilities

• Design and optimize Retrieval Systems for complex AI apps.
• Advance LLM context engineering and context assembly strategies.
• Build evaluation pipelines for retrieval relevance and factual grounding.
• Ensure production readiness with secure, grounded retrieval.

Skills

RAG

Information retrieval

LLM context engineering

Production AI systems

Semantic search

Vector search

Job description

Senior AI Systems Engineer (RAG)

$170000 - $200000 per year | South Salt Lake, UT | Remote | Permanent

Fully remote, well-funded AI startup. Excellent benefits, stable future, room for growth!

A bit about us:

We’re an early-stage AI startup on a mission to redefine how users interact with intelligent systems. We’re building something brand new and we’re looking for talent to help us scale our platform services.

Why join us?

• Fully Remote
• Well-funded
• Senior team of proven engineers
• Autonomy and upward mobility
• Competitive compensation
• Lucrative Equity
• Great healthcare benefits

Job Details

We’re seeking a Senior AI Systems Engineer with deep expertise in RAG, information retrieval, and LLM context engineering. This position focuses on LLMS receive the right information, in the right format, at the right time. You’ll take technical ownership of the systems responsible for identifying relevant information, retrieving it efficiently, and constructing high-quality context for downstream language models. The ideal candidate has built and optimized these systems in production, and understands the limitations of modern LLMs.

What You’ll Do

• Design and Optimize Retrieval Systems: Architect and improve RAG pipelines supporting complex AI applications. Develop sophisticated retrieval strategies using semantic search, optimize document ingestion, indexing, and retrieval across large datasets.
• Advance LLM Context Engineering: Design intelligent context assembly strategies that determine which information reaches the model and how it’s organized. Optimize context selection, ordering, compression, and token utilization to improve LLM response quality.
• Build Evaluation and Measurement Frameworks: Develop repeatable evaluation pipelines to measure retrieval relevance, answer quality, factual grounding, and system performance. Establish benchmarks and datasets to identify regressions and guide technical improvements.
• Engineer Secure, Production-Ready AI Systems: Implement retrieval architecture, ensure retrieved content is accurate, traceable, and appropriately grounded in source material.

What We’re Looking For

• Strong software engineering background with hands-on experience developing and maintaining production AI systems.
• Demonstrated expertise in RAG, information retrieval, semantic search, or LLM context engineering.
• Experience designing and operating retrieval or context management systems serving real users in production.
• Deep understanding of document chunking, embedding strategies, vector search, hybrid retrieval, indexing, and re-ranking.
• Strong knowledge of LLM behavior, including context-window limitations, information ordering, token management, and grounding.
• Ability to measure and optimize tradeoffs involving quality, latency, scalability, and infrastructure cost.
• Understanding of document-level authorization, data isolation, and secure information retrieval.

Jobot is an Equal Opportunity Employer. We provide an inclusive work environment that celebrates diversity and all qualified candidates receive consideration for employment without regard to race, color, sex, sexual orientation, gender identity, religion, national origin, age (40 and over), disability, military status, genetic information or any other basis protected by applicable federal, state, or local laws. Jobot also prohibits harassment of applicants or employees based on any of these protected categories. It is Jobot’s policy to comply with all applicable federal, state and local laws respecting consideration of unemployment status in making hiring decisions.

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