LLM Engineer Job Description Template (Skills, Requirements, Sample Copy)
A strong LLM engineer job description separates RAG-experienced candidates from API-calling generalists before the screening call. This template covers role overview, RAG and evaluation requirements, LangChain and vector database skills, and compensation ranges — formatted for immediate use. Remote LLM engineers from India through F5 start at $600/week all-inclusive. Shortlist in 7–14 days.
In summary
A strong LLM engineer job description separates RAG-experienced candidates from API-calling generalists before the screening call. This template covers role overview, RAG and evaluation requirements, LangChain and vector database skills, and compensation ranges — formatted for immediate use. Remote LLM engineers from India through F5 start at $600/week all-inclusive. Shortlist in 7–14 days.
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Most LLM engineer job descriptions describe what the company wants to build, not what skills will actually build it — which is why they attract developers who have used LLMs and not engineers who have deployed them. Writing one that screens candidates before the first call requires knowing which requirements separate production experience from tutorial experience, and which signals predict a successful hire rather than an impressive resume.
This article provides a complete, copy-pasteable LLM engineer job description template — every section written out, not summarized — along with a breakdown of what each job description element actually screens for and how F5 Hiring Solutions applies this framework when vetting LLM engineers for its clients.
What Does an LLM Engineer Job Description Need to Screen Effectively?
The core problem with most LLM engineer job descriptions is that they are aspirational rather than operational. They describe what the company will build ("a production RAG system") and what they hope the engineer will be ("an expert in large language models") without specifying what verifiable experience separates a qualified candidate from an enthusiastic one.
A well-structured LLM engineer job description does three things before the first screening call: it filters out developers with only API experience, it identifies candidates with production RAG exposure, and it signals to strong candidates that the company understands the role well enough to hire for it. Job descriptions that miss all three produce long pipelines with low conversion and slow time-to-fill.
According to LinkedIn's 2026 data, AI engineer postings grew 143% year-over-year — meaning the candidate pool for LLM roles is expanding faster than the qualified subset within it. The job description is the primary filter between the two.
| LLM JD Element | What to Screen For | Common Red Flag | Strong Signal |
|---|---|---|---|
| RAG pipeline experience | End-to-end production retrieval: chunking, embedding, indexing, retrieval quality metrics | Candidate describes chatting with PDFs using LlamaIndex without measuring retrieval quality | Candidate names the chunking strategy they chose and why (e.g., semantic vs. fixed-window) based on document type |
| Vector database requirement | Schema design decisions, not just familiarity with the tool name | "I've used Pinecone" with no follow-up on namespace design or metadata filtering | Candidate describes a specific index design tradeoff they made in a live system |
| Evaluation framework | Experience with Ragas, ARES, or a custom evaluation harness — not just BLEU/ROUGE | Candidate defines success as "the output looks right" or relies entirely on user feedback | Candidate can describe a retrieval recall vs. precision tradeoff and how they resolved it |
| LangChain / orchestration | Production orchestration experience, not tutorial completion | All examples are from LangChain quickstart docs with no customization described | Candidate describes a point where they moved away from a framework abstraction because it was too rigid |
| Prompt engineering | Systematic prompt iteration with a version-control approach, not informal tweaking | Candidate cannot explain how they tracked prompt changes or measured impact | Candidate describes a specific prompt failure and how they diagnosed whether it was a retrieval or generation problem |
| Compensation range | Filters misaligned expectations on both ends before the call | No range listed — attracts both overqualified and underqualified applicants equally | Specific range signals the company knows the market; attracts candidates who have already self-screened |
What Does the Full LLM Engineer Job Description Template Look Like?
The following is a complete, copy-pasteable job description template. Every section is written out. Placeholder text appears in brackets. Adjust role title, compensation, and company details before posting.
LLM Engineer — [Remote / Hybrid / On-Site]
[Company Name] | [Location or Remote] | [Full-Time / Contract]
About the Role
[Company Name] is building [one-sentence description of the product or system]. We are hiring an LLM Engineer to design, build, and maintain the language model infrastructure that powers [core feature or capability].
This role is focused on production RAG systems and LLM-powered agents — not research or model training. You will work directly with [engineering lead or CTO/VP Eng] and own the full retrieval and generation stack from design through deployment and evaluation.
What You'll Build
- A production RAG pipeline that indexes [document type or data source] and serves retrieval-augmented responses to [users/internal tools/API consumers]
- LLM-powered agents capable of [specific task: e.g., multi-step document review, structured data extraction, or tool-use across internal APIs]
- An evaluation harness that measures retrieval quality, answer faithfulness, and hallucination rate on a scheduled cadence
- Prompt versioning and experimentation infrastructure to support systematic improvement of generation quality
- Monitoring and alerting for latency, token cost, and output quality regressions in production
Required Skills
- RAG pipeline experience: You have built at least one production retrieval-augmented generation system end-to-end — from document ingestion and chunking strategy through embedding, vector indexing, retrieval, and generation. You can describe the retrieval quality metrics you used.
- LangChain or equivalent orchestration: You have used LangChain, LlamaIndex, or a comparable framework in a production environment. You understand where these abstractions help and where they create constraints.
- Vector databases: You have worked with at least one of Pinecone, Weaviate, Qdrant, pgvector, or Chroma in production. You can describe a schema or namespace design decision you made and why.
- LLM API integration: You are comfortable working with OpenAI, Anthropic, Google Gemini, or open-weight models via Hugging Face Inference API. You understand token limits, context window management, and cost implications.
- Evaluation frameworks: You have used Ragas, ARES, or built a custom evaluation harness. You understand the difference between retrieval recall and answer faithfulness and can explain how you measured each.
- Python proficiency: Your primary language is Python. You write clean, testable code and are comfortable with async patterns relevant to LLM workloads.
- Prompt engineering discipline: You treat prompts as versioned artifacts, not informal text. You can describe how you structure prompt iterations and measure improvement.
Nice-to-Have
- Fine-tuning experience with LoRA or QLoRA on open-weight models (Llama, Mistral, Phi)
- Hugging Face model hub familiarity — uploading, versioning, or pulling models programmatically
- Experience with agentic frameworks (LangGraph, AutoGen, CrewAI) for multi-agent orchestration
- Familiarity with observability tools for LLM workloads (LangSmith, Helicone, Arize Phoenix)
- Experience deploying model inference endpoints (vLLM, TGI, Ollama)
- Contributions to open-source LLM tooling or published evaluation benchmarks
What We Offer
- Compensation: [$X–$Y per year / $X–$Y per week, depending on model]
- Location: [Fully remote / hybrid — specify days and location]
- Equity: [Percentage or options range, or "none for this role"]
- Benefits: [Health, dental, vision details / or "full benefits package"]
- Equipment: [Company-provided or stipend amount]
- Growth: You will be the [first / second / third] LLM engineer on the team. The roadmap includes [briefly describe next major project].
About [Company Name]
[Company Name] [founded year, location if relevant] [what the product does in one sentence]. We serve [customer type] and have [traction signal: users, revenue, funding, enterprise clients — use only real numbers]. Our engineering team is [size], and this role reports to [title of manager].
We believe [one sentence on engineering culture — e.g., "in small teams with clear ownership" or "in shipping fast and measuring everything"]. You will have direct input on architecture decisions from day one.
To apply: [Link or email] | Include a link to a GitHub repository, deployed system, or written description of a RAG or LLM project you built.
How Should You Use This LLM Engineer Job Description Template?
Copy the template above and fill every bracketed placeholder before posting. A job description with empty brackets or vague placeholders signals to strong candidates that the role is not well-scoped — and strong LLM engineers have options.
A few section-specific notes on applying this template:
About the Role: Be specific about what the LLM stack will power. "AI features" is not a description. "A retrieval system that answers insurance policy questions from 400,000 PDF documents" is. Specificity attracts candidates who have worked on similar problems.
What You'll Build: This section does more filtering work than the requirements list. Engineers who read "evaluation harness that measures hallucination rate" and understand what that means are the engineers you want. Engineers who skip past it are not.
Required Skills: Do not add requirements you cannot evaluate in an interview. If you list "LangGraph experience" but your interview process does not assess it, you will reject qualified candidates for missing a requirement that was never going to matter.
What We Offer: The compensation section is the most skipped and the most consequential. According to Stanford's AI Index 2026, agentic AI postings grew 280% year-over-year — meaning strong LLM engineers receive multiple offers. A specific range closes more pipelines than a vague "competitive salary."
Application instruction: Requiring a GitHub link or project description at application stage performs passive screening. Candidates without production LLM work cannot provide it; candidates with production work will appreciate that you asked.
How Does This Template Compare to Standard Engineering Job Descriptions?
| LLM JD Element | What to Screen For | Common Red Flag | Strong Signal |
|---|---|---|---|
| Role definition | Clear distinction between LLM application engineering and ML research/training | "Build and train LLMs" — conflates application engineering with research | "Build RAG pipelines and LLM agents on top of existing models" — accurate scope |
| Tech stack specificity | Named tools (Pinecone, LangChain, Ragas) rather than categories ("AI tools") | "Experience with AI/ML tools" — attracts anyone who has used ChatGPT | "Pinecone or Weaviate, LangChain or LlamaIndex, Ragas" — candidates self-screen accurately |
| Evaluation requirement | Explicit mention of RAG evaluation (retrieval quality + answer faithfulness) | No evaluation requirement — implies the company will accept "it looks good" as success | Named evaluation frameworks signal that quality measurement is a first-class concern |
| Application requirement | Portfolio evidence (GitHub, deployed system, written project description) | Resume-only — cannot distinguish tutorial experience from production experience | Requiring a project link filters out candidates with no deployable work |
How Does F5 Apply This Framework When Vetting LLM Engineers?
When a client engages F5 Hiring Solutions to hire vetted remote LLM engineers, the job description template above maps directly to the screening criteria F5 applies before a candidate reaches the client's shortlist.
F5's LLM engineer vetting process runs in three stages. The first is a structured technical intake — candidates complete a written assessment covering chunking strategy decisions, retrieval quality measurement, and prompt versioning approach. This alone eliminates the majority of applicants who have API experience but not production RAG experience.
The second stage is a live technical interview with F5's in-house AI/ML screening team. Candidates walk through a RAG system they built, describe the evaluation framework they used, and answer diagnostic questions about failure modes — hallucination, retrieval drift, context window overflow — and how they detected and resolved each.
The third stage is a client-facing introduction, by which point every candidate on the shortlist has cleared both gates. Clients using this model report dramatically shorter evaluation cycles because the first-round technical questions are already answered.
F5 draws from 85,500+ candidates in its internal sourcing and screening database and delivers shortlists within 7–14 business days. Remote LLM engineers from India and the Philippines are placed at $500–$950/week all-inclusive — within the full canonical range of $375–$1,200 per week, all-inclusive — covering salary, equipment, payroll, HR, and performance management. If a hire does not work out at any point, F5 replaces them within 7–14 days at zero cost.
For SaaS and technology companies scaling AI capabilities, see F5's SaaS and technology hiring guide for context on how LLM engineering fits into broader technology team structures.
For a detailed breakdown of what qualifications and signals to evaluate beyond the job description, read what to look for when hiring an LLM engineer, which covers the interview and assessment layer in detail.
For a breakdown of how F5's pricing compares to direct hiring for AI roles, visit compare remote hiring pricing.
Understanding how F5's managed remote workforce model differs from traditional recruiting or EOR arrangements is covered at how F5 managed remote workforce works.
Frequently Asked Questions
What is the difference between an LLM engineer and a machine learning engineer?
Should I require a degree in the LLM engineer job description?
What vector databases should an LLM engineer know?
How do I screen LLM engineers for RAG experience specifically?
What compensation should I list in an LLM engineer job description?
How long does it take to hire an LLM engineer through F5?
What evaluation frameworks should an LLM engineer know?
Can I use this job description template for a contract LLM engineer role?
Ready to Shortlist Vetted LLM Engineers in 7–14 Days?
F5 Hiring Solutions places dedicated, full-time remote LLM engineers from India and the Philippines at $500–$950/week all-inclusive — every cost included, no placement fees, no recruiting markups, and a replacement guarantee that applies anytime at zero cost.
View LLM and AI/ML engineer placement details or book a call directly with Joel Deutsch to discuss your stack and timeline:
Frequently Asked Questions
What is the difference between an LLM engineer and a machine learning engineer?
An ML engineer trains and tunes models from scratch. An LLM engineer builds applications on top of pre-trained large language models — RAG pipelines, agents, prompt chains, and evaluation frameworks. The skills overlap at the infrastructure layer but diverge sharply at the application layer.
Should I require a degree in the LLM engineer job description?
No. A degree requirement will eliminate most strong candidates. LLM engineering is a post-degree field — the best practitioners learned through open-source projects, Hugging Face contributions, and production deployments. Require demonstrated RAG or agent work instead, using GitHub or a take-home task.
What vector databases should an LLM engineer know?
Pinecone, Weaviate, Qdrant, and pgvector are the most common in production stacks. Chroma is common in prototypes. Knowing one well is more valuable than surface familiarity with all of them. Ask candidates to describe a schema design decision they made in a real system.
How do I screen LLM engineers for RAG experience specifically?
Ask them to describe a retrieval pipeline they built end-to-end: chunking strategy, embedding model selection, index design, and how they measured retrieval quality. Candidates who cannot explain retrieval quality metrics have not shipped a production RAG system.
What compensation should I list in an LLM engineer job description?
U.S.-based LLM engineers command $160K–$280K base salary at mid-to-senior levels. Remote LLM engineers from India through F5 Hiring Solutions start at $500–$950/week all-inclusive, with the full canonical range running $375–$1,200 per week across all roles. Listing a range attracts serious candidates and filters out misaligned expectations.
How long does it take to hire an LLM engineer through F5?
F5 delivers a shortlist of vetted LLM engineer candidates within 7–14 business days. Engineers typically start within 30 days of engagement. If a hire does not work out for any reason, F5 replaces them within 7–14 days at zero cost.
What evaluation frameworks should an LLM engineer know?
Ragas and ARES are the most used RAG evaluation frameworks. For general LLM output quality, candidates should know how to design human evaluation rubrics and automated scoring with reference models. BLEU and ROUGE are known but rarely sufficient for LLM tasks — a strong candidate will say so.
Can I use this job description template for a contract LLM engineer role?
Yes with one change: replace the employment-type language in the offer section and remove equity mentions. The skills, requirements, and evaluation criteria apply equally to full-time and contract LLM engineers.