AI Recruitment Agency for Startups.
Hire genuine Machine Learning, GenAI, LLM, and Agentic AI engineers who build reliable production systems—not superficial API wrapper builders.
Separating genuine AI engineering from tutorial hype.
The rapid explosion of generative AI has flooded the hiring market with candidates who have experimented with popular APIs or followed quick courses, but lack foundational engineering depth.
Evaluation & Benchmarking Gaps
Many AI developers can prompt a model, but few understand how to establish quantitative eval frameworks (ragas, LLM-as-a-judge) to measure production drift and accuracy.
Latency & GPU Cost Inefficiencies
Deploying inference pipelines that scale without astronomical cloud spend requires deep expertise in vLLM, TensorRT-LLM, quantization, and semantic caching.
Agentic Loop Failures
Building autonomous agents that reliably recover from tool errors, state hallucinations, and infinite recursive loops is significantly harder than calling standard endpoints.
AI Profiles We Map & Deliver
Focused candidate shortlists calibrated across both research depth and production systems architecture.
Agentic AI & LLM Systems Engineer
Autonomous agent workflows, LangGraph, tool calling, memory management, and structured JSON reliability.
Machine Learning & MLOps Engineer
Feature pipelines, model training/fine-tuning (LoRA, QLoRA), embedding indices, and low-latency serving.
Computer Vision / Deep Learning Specialist
Multi-modal perception, object detection, diffusion architectures, and edge deployment.
Head / VP of Artificial Intelligence
Technical leadership defining AI product roadmaps, IP architecture, compute budgets, and research talent attraction.
How we evaluate AI technical signal.
We evaluate GitHub open-source contributions, Hugging Face models, technical architecture explanations, and actual engineering decisions before presenting a candidate.
Architecture Probe
Can the candidate explain how their RAG pipeline handles complex multi-hop queries and retrieval re-ranking?
Code & Eval Depth
Do they maintain reproducible evaluation datasets or rely purely on subjective gut feeling?
Production Reality
Have they dealt with model degradation, token cost spikes, rate limiting, and fallback routing in live production?
Startup Velocity
Comfort with rapidly evolving open-source frameworks, shipping weekly, and high ambiguity.
AI Recruitment FAQs
Specific considerations for hiring AI and ML engineering talent.
What is the typical timeline for an AI engineering shortlist?
For priority AI mandates with calibrated compensation, our 48-hour shortlist capability delivers initial pre-screened candidate dossiers within 48 to 72 hours.
How do you source passive AI talent?
We systematically track open-source AI contributors, authors of technical model implementations, Kaggle Grandmasters, and engineers currently building at foundational AI labs.
What compensation benchmarks apply to senior AI talent in India?
Top-tier AI engineers in India command competitive fixed packages (₹35L–₹75L+ depending on seniority) plus substantial equity incentives. We benchmark ranges during intake to ensure immediate market alignment.
Discuss Your AI Mandate
Tell us your model requirements and engineering context. We will share a talent availability assessment.
Discuss AI & Engineering Mandate
Let us understand what you are hiring for. We will review our network and share a realistic delivery capability.