The AI talent market is brutally competitive. Kovil AI pre-screens AI engineers, LLM specialists, and ML engineers so you can extend your team with senior AI talent in days, not months. Your management, your tools, your roadmap.
LLM engineer job postings grew 189% in 2025. Senior AI engineers are fielding 5-10 recruiter messages per week. The gap between demand and supply is widening, not closing — because training AI engineers takes years and the field is moving too fast for traditional education to keep up.
For most companies, waiting 3-6 months to hire an AI engineer means missing the market window. A competitor ships an AI feature. A prospect chooses a product that already has AI. The opportunity cost is real.
AI staff augmentation solves this by giving you access to a pre-vetted bench of AI engineers who are ready to start in 2 weeks. The hiring risk, the vetting effort, and the talent search are done. You just interview the final 2-3 candidates and choose.
Six specialist AI roles, each with a dedicated vetting track. Every candidate is assessed on production-grade AI experience, not just theoretical knowledge.
Four stages. Less than 3% pass. You only interview candidates who have already cleared every gate.
Role-specific AI engineering assessment: LLM API task, RAG design question, and architecture scenario. Timed and auto-scored.
60-minute interview with a Kovil AI senior engineer. Code review, system design, AI architecture deep-dive. We see how they think, not just what they know.
We assess English fluency, async written communication, ability to explain complex AI concepts to non-technical stakeholders, and remote collaboration habits.
We speak directly with former managers or tech leads. We verify specific AI project contributions, delivery reliability, and team integration quality.
2-week trial period on every placement. Not the right fit? We replace at no cost.
Start the BriefRAG engineer? LLM fine-tuning specialist? AI tech lead? Brief us and we will have pre-screened candidates ready in 48 hours.
Brief Us NowFour common scenarios where AI staff augmentation solves the problem faster than any other model.
Scenario 1
You are shipping an AI feature in Q3 but have no LLM engineers
Add 1-2 AI engineers for the sprint. They design the architecture, build, and hand over to your team with docs.
Scenario 2
Your RAG chatbot is performing poorly and your team does not know why
Bring in an AI engineer to audit the retrieval pipeline, tune re-ranking, and set up RAGAS eval. Fixed in weeks, not months.
Scenario 3
You hired an AI engineer 6 months ago but velocity is low and quality is inconsistent
Augment with a senior AI tech lead who can upskill your hire, improve the architecture, and pair-program on the hard problems.
Scenario 4
You want to fine-tune a model on your proprietary data but have no ML team
Bring in an LLM engineer with LoRA/QLoRA experience for a focused 3-month fine-tuning engagement.
A healthcare SaaS platform needed to extract structured data from unstructured clinical notes. Their engineering team was strong on the backend but had zero NLP or LLM experience. We matched a senior LLM engineer with healthcare NLP background in 48 hours. He joined their GitHub, their Slack, and their sprint ceremonies. Within 10 weeks he had shipped a production extraction pipeline processing 15,000 notes per day with 91% field-level accuracy.
AI staff augmentation is extending your existing engineering team with specialist AI talent — LLM engineers, AI engineers, ML engineers, and generative AI developers — who work under your management on your projects. You retain full control over the work while gaining skills your team does not currently have, without the 3-6 month timeline of a full-time hire.
Kovil AI provides AI engineers (LangChain, RAG, agents), LLM engineers (fine-tuning, RLHF, LLMOps), generative AI developers (GPT-4o/Claude integrations, prompt engineering), ML engineers (PyTorch, model training, MLOps), and AI-fluent full-stack engineers who can build the full application layer around an AI backend.
Our AI-specific vetting goes beyond general coding ability. We test: LLM API integration proficiency (token management, streaming, structured output), RAG system design (chunking strategies, vector store selection, retrieval evaluation), agent architecture (LangGraph, tool use, loop control), fine-tuning methodology (LoRA, QLoRA, dataset preparation), and production deployment experience (LangSmith, monitoring, cost management). Less than 3% of AI applicants pass.
We present screened candidates within 48 hours of your brief. Typical end-to-end time from brief to engineer starting: 10-14 days, accounting for your interview schedule, selection, and access provisioning. Compare this to 3-6 months for a full-time AI engineer hire in the current market.
Augmented AI engineers are selected for their ability to work autonomously within your team structure. They can define AI architecture, make technology choices, and implement independently. If your team has no AI context at all, we recommend starting with an AI tech lead who can set direction before adding more engineers. We will advise the right structure during the brief.
A senior AI engineer costs $160K-$200K+ annually in base salary in the US, plus 20-30% in benefits, taxes, and employer costs — totalling $195K-$260K per year fully loaded. AI staff augmentation via Kovil AI provides comparable seniority at lower total cost, with no benefits overhead, no 4-month recruitment timeline, and the ability to scale down when the AI project phase ends.
Yes. Many clients bring in augmented AI engineers specifically to start greenfield AI projects that the existing team does not have the skills to build. A common pattern: 1 AI tech lead sets architecture and makes core decisions, 1-2 AI engineers build and implement, and your existing team handles integrations with the rest of the product. The AI team hands over the system with documentation and a knowledge transfer session.
Our minimum engagement is 3 months. AI projects rarely benefit from shorter windows — there is significant ramp-up time for context, and the first month is often spent on architecture and foundational work before meaningful feature velocity kicks in. Most AI staff augmentation engagements run 3-12 months, with many clients retaining engineers on a rolling basis as their AI roadmap expands.
Tell us the AI role, the skills, and your timeline. Pre-screened candidates in 48 hours. Two-week trial on every placement.
Brief Us Now