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AI Engineer with 2+ years in LLM-based applications & FastAPI.
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI / Backend Engineer with ~2 years of experience building applied AI systems and backend services. Focused on LLM-based applications, RAG pipelines, document intelligence systems, and API-first architectures using FastAPI. Experienced in designing and deploying full-stack AI products, including hybrid inference workflows, authentication systems, and real-time AI services.
ESSAI
Data Science Engineering · Data Science Engineering
August 1, 2021 – June 30, 2024
ESSTHS
Mathematics & Physics Pre-Engineering · Mathematics & Physics
August 1, 2018 – June 30, 2021
Freelance
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June 29, 2026 – Present
• Built an AI SaaS platform for CV optimization based on job descriptions. • Designed a FastAPI backend with JWT refresh token rotation, OTP verification, CAPTCHA, and rate limiting. • Built a separate AI microservice with vision LLM pipeline (PDF->image->structured extraction) via LangChain + Groq API, instrumented with Langfuse tracing for observability. • Implemented async job queue (ARQ/Redis) for background CV processing with Redis Pub/Sub->SSE streaming for real-time frontend updates. • Deployed containerized backend + worker on Render, frontend on Vercel.
Cultural Fit Analysis
The candidate's experience across freelance, R&D, and internship roles, coupled with a personal project, demonstrates initiative and adaptability. The diverse set of technologies and problem domains (CV optimization, chatbots, document extraction, routing) suggests a broad interest in AI applications and a willingness to tackle varied challenges. The mention of multilingual search data (Ar, Fr & En) indicates an appreciation for diverse user bases and internationalization, which can be a positive cultural fit.
Soft Skills & Operational Fit
The candidate's resume indicates experience in mentoring junior engineers and collaborating with cross-functional teams, suggesting good teamwork and leadership potential. The project descriptions are clear, indicating good communication skills in a technical context. The focus on building end-to-end AI products aligns well with operational needs for deploying robust systems.