Help & Supportcareers@gravityer.com
AI Engineer with 1+ years in Generative AI & MLOps
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Results-driven AI Engineer with production experience designing, developing, and deploying enterprise-grade Generative AI, Retrieval-Augmented Generation (RAG), Multi-Agent Systems, and Computer Vision solutions. Proficient in optimizing open-source and proprietary Large Language Models (LLAMA-3, Gemini, GPT) utilizing frameworks including CrewAI, LangChain, and Hugging Face. Microsoft Azure AI Engineer Associate (AI-102) certified with strong expertise in scalable MLOps pipelines, model evaluation (RAGAS), and end-to-end model deployment.
Cultural Fit Analysis
The candidate's projects demonstrate a breadth of application areas for AI, from meeting intelligence and talent evaluation to multimedia summarization and medical diagnostics. This diversity, coupled with certifications in Azure, AWS, MLOps, and Prompt Engineering, suggests a proactive learning attitude and adaptability. The target role of 'AI Engineer' aligns well with the candidate's stated skills and professional experience, indicating a good cultural fit for a technically demanding and evolving role.
Soft Skills & Operational Fit
The resume indicates experience in cross-functional collaboration to enhance model response latency and ensure production system reliability. The candidate's project descriptions suggest an ability to work on end-to-end solutions and integrate AI microservices into existing systems, which implies good operational fit. However, without psychometric or English test scores, a comprehensive assessment of soft skills and operational fit is limited.