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AI Engineer with less than a year in DevOps & GenAI
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Software Engineer with hands-on experience building AI-powered systems, GenAI pipelines, and DevOps automation platforms. Skilled in Python, FastAPI, and containerization with Docker and Kubernetes, with a strong foundation in MLOps tooling including Airflow, MLflow, and DVC. Passionate about building intelligent systems that are production-ready, scalable, and solve real engineering problems.
FAST National University of Computer and Emerging Sciences (NUCES)
Bachelor of Science · Software Engineering
August 1, 2022 – June 30, 2026
DevOps AutoPilot – AI Deployment Platform (FYP)
June 1, 2022 – June 1, 2026
Built full-stack AI-powered DevOps platform that automates Docker and Terraform configuration generation. Integrated local LLM (Qwen2.5-Coder via Ollama) for intelligent Dockerfile and docker-compose generation with SSE streaming. Developed hybrid heuristic and ML analysis pipeline detecting language, framework, ports, databases, and services automatically. Implemented multi-service detection for monorepo projects with per-service Dockerfile generation. Built React/TypeScript frontend with real-time build/deploy progress streaming and Docker management UI.
MLOps Real-Time Predictive System (RPS)
June 1, 2022 – June 1, 2026
Built end-to-end MLOps pipeline for real-time predictive modeling with automated data ingestion and model retraining. Orchestrated ETL workflows using Apache Airflow DAGs with scheduled data extraction from live APIs. Versioned datasets with DVC and tracked experiments via MLflow integrated with Dagshub. Established CI/CD pipeline with GitHub Actions and CML for automated testing and model comparison. Containerized ML model serving API using Docker with FastAPI, deployed monitoring stack with Prometheus and Grafana.
Async Standup Agent – AI Team Communication
June 1, 2022 – June 1, 2026
Developed multi-stage AI pipeline transforming voice recordings into structured team digests with cross-team blocker detection. Integrated OpenAI Whisper for local speech-to-text and Google Gemini for structured JSON extraction. Built semantic clustering using Gemini embeddings and Agglomerative Clustering for thematic grouping. Automated daily delivery to Slack channels and Notion pages with APScheduler cron jobs.
Cultural Fit Analysis
The candidate's academic projects showcase a strong interest in AI, MLOps, and DevOps, which aligns well with an AI Engineer role. The diversity of projects (AI deployment platform, real-time predictive system, AI team communication) indicates a broad technical curiosity and ability to apply AI/ML concepts to different problem domains. The use of modern tools and frameworks suggests an eagerness to learn and adopt industry best practices. However, without information on collaborative work environments or contributions to open-source, a full cultural fit assessment is limited.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive approach to problem-solving and a focus on building production-ready, scalable systems. The academic projects demonstrate an ability to work on complex, multi-faceted problems, which suggests good operational fit for challenging technical roles. However, without direct work experience or psychometric test results, it's difficult to fully assess soft skills like teamwork, communication in a professional setting, or stress handling.