Retrieval and grounding
Hire RAG & Context Engineers who make AI answer from your data.
Get a dedicated RAG & Context Engineer to build ingestion, chunking, retrieval, reranking, citations, grounding checks, and refusal behavior for production AI systems. Shortlist in 48 hours. Two-week paid trial in your codebase. Starts at $2,500/mo.
Direct answer
What does RAG & Context Engineer own?
A RAG & Context Engineer is the right hire when your AI product needs accurate answers from private, changing, or permissioned data. This role owns ingestion, chunking, embeddings, hybrid search, metadata filters, reranking, context assembly, grounding, citations, refusal behavior, and retrieval evaluation.
Hiring problem
Hire this role when answers need to be grounded in private or permissioned data.
The assistant sounds confident, but it cannot prove the answer, misses relevant context, retrieves stale documents, leaks permissioned data, or hallucinates over weak evidence.
- Data ingestion
- Chunking strategy
- Embeddings and hybrid search
- Metadata filters
- Reranking
- Context assembly
- Permission boundaries
- Citation behavior
- Retrieval evaluation
- Refusal/clarification behavior
- General model tuning
- Product UI only
- Multi-step agents using tools
- Generic backend implementation without retrieval quality ownership
First 14-day proof
The trial should create evidence, not just activity.
Ingestion audit
Shows source freshness, duplication, malformed documents, access boundaries, and parsing issues. Most retrieval failures start here, so it is the first thing inspected.
Chunking and metadata plan
Explains how documents should be split, labeled, filtered, and preserved. A weak plan shreds tables and context; a strong one keeps meaning retrievable.
Relevance eval set
Creates questions, expected sources, and pass/fail criteria so retrieval quality is measurable instead of anecdotal.
Retrieval baseline
Measures current retrieval performance before any architecture change. It proves whether later work actually helped or just felt better.
Grounding and citation design
Defines how answers cite sources, refuse weak evidence, and explain uncertainty. This is what makes an answer defensible to a customer or auditor.
Failure taxonomy
Groups retrieval failures: missing source, stale source, wrong chunk, no permission, weak rerank, bad citation. It points fixes at the real cause.
Permission risk review
Identifies where users may see context they should not, or miss context they need. It catches the data-exposure problems that block enterprise launch.
Default stack
Stack fluency for RAG & Context Engineer work.
The exact tools follow your environment. These are the common surfaces we vet against for this role.
Use cases
Where this hire creates leverage.
The best use case is one where the role can own a clear first proof during the paid trial.
Internal knowledge assistant
Answer employee questions from policies, docs, tickets, and internal knowledge. The proof is a relevance eval set plus citations your team can spot-check.
Customer support assistant
Ground support answers in approved docs and past cases so replies are accurate and traceable. Add an AI Application Engineer to productize the agent UX.
Legal and compliance document assistant
Handle high-stakes documents with citations and explicit refusal behavior. A confident wrong answer here is a liability, so refusal design comes first.
Sales enablement assistant
Retrieve product, pricing, objection, and account context safely, respecting who is allowed to see what.
Research assistant
Find and synthesize evidence across changing sources without losing the trail back to where each claim came from.
Permission-aware enterprise search
Respect roles, groups, tenants, regions, and document-level access. Add an AI Security Engineer if leakage risk is the gate to release.
Transparent pricing
Pick seniority by ownership, not mystery quotes.
Supervised delivery for clear implementation work.
Independent feature ownership for production AI work.
High-judgment ownership for ambiguous or risky AI delivery.
Outcome clarity
What should change after you hire this role?
Retrieval quality has a baseline.
Answers show evidence and refusal behavior.
Permission risks are visible before launch.
Adjacent-role comparison
When another AI role is the better hire.
LLM Engineer
Choose LLM Engineer if the problem is model behavior/eval beyond retrieval.
SECAI Security Engineer
Choose AI Security Engineer if prompt injection or data leakage is blocking release.
PLTAI Platform Engineer
Choose AI Platform Engineer if RAG must become shared infrastructure.
APPAI Application Engineer
Choose AI Application Engineer if product UX is the blocker.
Vetting criteria
Screened for this role’s failure modes.
Retrieval evals
Chunking and metadata judgment
Permission-aware context
Citation and refusal design
Document ingestion reliability
Interview questions
Use the interview to test judgment.
- How would you create a retrieval eval set?
- What makes chunking fail?
- How do you prevent permission leakage?
- When should a system refuse instead of answer?
Hiring flow
From scope to paid trial.
30-minute role scope
Map the AI workflow, current stack, first deliverable, security boundaries, seniority, and the role that should own the work.
2-3 vetted engineers
Receive a short list with matching rationale. The goal is fewer names with stronger fit, not resume volume.
Paid trial in your codebase
The selected engineer works inside your repo, rituals, issue tracker, and review process so fit is judged by real work.
Continue, replace, pause, or scale
Continue month-to-month, request a free replacement, pause without a long lock-in, or add adjacent roles.
Security, IP, governance
Repo access is scoped before the engineer starts.
NDA, IP assignment, repository access, communication channels, data boundaries, and AI tool rules are clarified before onboarding. Devlyn avoids unverified compliance claims and works within buyer-controlled systems.
FAQ
Questions before you hire RAG & Context Engineer.
Why is RAG a specialist role?
Because retrieval quality depends on ingestion, chunking, metadata, permissions, reranking, evals, and refusal behavior, not a vector database alone.
Can this role use our current vector database?
Yes. The work starts with retrieval quality and risk, not a forced vendor switch.
What should they prove in two weeks?
An ingestion audit, relevance eval set, retrieval baseline, citation behavior, and a clear failure taxonomy.
How fast can I see RAG & Context Engineer candidates?
After the role scope, Devlyn targets two or three vetted profiles within 48 hours.
What does the two-week paid trial include?
The trial should produce role-specific proof for RAG & Context Engineer work inside your actual repo, data environment, or approved workflow.
Can the engineer work in our repository?
Yes. Repo access, communication channels, data boundaries, NDA, and IP assignment are scoped before onboarding.
What if fit is wrong?
You can request a free replacement instead of being forced through a long lock-in or conversion fee.
What does pricing include?
Pricing covers one dedicated AI-native engineer. Junior starts at $2,500/mo, mid at $3,500/mo, and senior at $4,500/mo.
Final CTA
Tell us the AI workflow. We’ll confirm whether RAG & Context Engineer is the right hire.
If another role is a better fit, the role scope should catch that before you interview anyone.