EU engineers, ready to place with your US clients

Pre-screened on AI. Remote B2B contracts. View 5 full profiles free — AI score, skills report, interview questions included.

Executive Summary

This candidate is a seasoned ML practitioner with a decade of experience and a strong recent pivot into foundation models, RAG systems, and LLM fine-tuning that directly aligns with this role. their work at Edge Group (UAE) and Revolut (UK — Exceptional Talent Visa) demonstrates both technical depth and the ability to deliver production-grade AI systems with measurable business impact. The primary gaps are the absence of a public code presence and limited visibility into their PyTorch/inference optimization depth, which warrants a structured technical interview. Given their experience range and current seniority, they fits comfortably within the $130k–$155k salary band. This candidate is recommended as a FIT candidate pending technical validation.

Top Strengths

✓Direct, recent experience building production RAG systems with multimodal inputs (GPT-4, LLaMA 3.2, CLIP) at Edge Group
✓Fine-tuning foundation models (ControlNet with LoRA/PEFT) with demonstrated business outcomes (18% ARPU increase)
✓10 years of consistent ML progression across high-profile companies (Revolut — UK Exceptional Talent Visa endorsed, Paytm, UnitedHealth)
✓Experience with vector databases (FAISS, Milvus) and ensemble retrieval strategies directly matching role requirements
✓Track record of deploying ML at scale with measurable impact across fraud detection, ad tech, and NLP domains

Key Concerns

!No public code presence (GitHub, open-source) makes technical depth difficult to independently validate
!No LinkedIn profile and no cover letter reduce transparency into motivation, communication style, and professional network verification

Culture Fit

72%

Growth Potential

High

Salary Estimate

$130k-$155k

Assessment Reasoning

Karun meets or exceeds the required 3–7 years of relevant experience (6+ years directly in ML/AI, 2+ years in LLMs/RAG), demonstrates hands-on work with the majority of required skills including RAG, LLM fine-tuning with LoRA/PEFT, vector databases, and production ML deployment. their recent role at Edge Group is a near-direct match to the job description's core responsibilities. The absence of LinkedIn and GitHub profiles prevents full confidence scoring but does not negate the strong resume substance. they clears the 80% required skills threshold and shows no major red flags, placing him firmly in FIT territory with a recommendation for a technical screen to validate PyTorch depth and inference optimization knowledge before advancing.

Interview Focus Areas

Deep dive into RAG architecture decisions at Edge Group — retrieval strategies, chunking, re-ranking, and evaluation methods usedPyTorch proficiency and transformer internals — attention mechanisms, positional encoding, and hands-on fine-tuning workflowsModel optimization experience — quantization (INT8/INT4), distillation, or inference acceleration techniques (vLLM, TensorRT)Remote work style, async communication habits, and experience in distributed team environments

Code Review

FairSenior Level

No GitHub profile or code samples were provided, making direct code quality assessment impossible. Based solely on resume descriptions, Karun appears capable of production-level ML engineering with deployment experience across multiple frameworks. A technical assessment or take-home exercise would be strongly recommended before making a final hiring decision.

+Demonstrated ability to deploy production microservices (Flask, Docker, REST APIs) inferred from resume
+Experience with distributed computing frameworks (Spark, Apache Beam) suggests strong engineering fundamentals

-No GitHub profile provided — cannot assess actual code quality, style, or open-source contributions
-No portfolio, personal projects, or code samples available for technical evaluation
-Absence of public code presence is a gap for a foundation model engineering role where community contribution is valued

Experience Overview

10y total · 6y relevant

This candidate has ~10 years of progressive ML/DS experience with the last 2+ years directly involving LLMs, RAG pipelines, and foundation model fine-tuning at Edge Group in Dubai. their experience at Revolut and Dailyhunt further demonstrates production-grade ML system design. The resume strongly aligns with the core requirements, though explicit evidence of PyTorch usage and advanced model optimization techniques (quantization, distillation) is not surfaced.

Matching Skills

Large Language Models (LLMs)RAG (Retrieval-Augmented Generation)Vector Databases (FAISS, Milvus)Fine-tuning (LoRA/PEFT)PythonTransformers ArchitectureHugging Face / LLaMA modelsPrompt EngineeringModel OptimizationAWS / GCP cloud infrastructure

Skills to Verify

Explicit PyTorch proficiency (not directly stated)LangChain / LlamaIndex (not mentioned)Docker & Kubernetes (partial - Docker mentioned at Revolut)Weights & Biases or equivalent experiment trackingExplicit inference optimization/quantization experience

Candidate information is anonymized. Personal details are hidden for fair evaluation.

Foundation Model Engineer