AI Engineer with less than a year in GenAI & MLOps.
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Machine Learning Engineer with an MSc in AI/ML and practical experience moving predictive models and GenAI applications into enterprise production. I specialize in building reliable, backend architectures using Python, FastAPI, and PostgreSQL to connect advanced LLM reasoning workflows with live relational databases. My work focuses heavily on engineering robust data preparation pipelines, deploying optimized RAG architectures through LangChain and LangGraph, and setting up automated CI/CD releases with Docker. I place high priority on operational health, utilizing AWS and MLOps monitoring tools to ensure models continuously provide accurate, stable support for real-world business decisions.
Jamia Hamdard, India
BTech · Computer Science Engineering (Artificial Intelligence)
N/A – June 30, 2024
University of Limerick, Ireland
MSc · Artificial Intelligence and Machine Learning
N/A – June 30, 2025
Expentor
AI/ML INTERN
March 1, 2026 – Present
USA
Hospital Management Dashboard with AI Assistant
June 24, 2026 – Present
Built a full-stack enterprise dashboard with a low-latency chat interface and FastAPI backend. Implemented PostgreSQL querying and FAISS vector indexing across 55,000+ records to support semantic retrieval and business Q&A.
SmartPhoto Mockup Generator
June 24, 2026 – Present
Maintained production stability for an image pipeline serving 10K+ monthly requests, using CloudWatch monitoring to support 99.9% uptime. Automated CI/CD workflows with Docker and GitHub Actions, improving deployment reliability by 15% and reducing downtime by 20%.
Autonomous Multi-Agent AI Support Orchestrator
June 24, 2026 – Present
Engineered a stateful 3-agent execution workflow utilizing a hybrid search pipeline combining dense vector retrieval (Qdrant) and sparse keyword retrieval (BM25) to maximize exact-match context extraction. Integrated a BGE-Reranker model to validate and score context fragments prior to LLM generation, reducing factual hallucination errors by 25% while maintaining strict sub-200ms processing latency limits.
Introduction to AI Agents
DataCamp
January 1, 2026 – Present
AWS Academy Graduate: Machine Learning Foundations & Cloud Foundations
AWS
January 1, 2025 – Present
Oracle Cloud Infrastructure (OCI) Generative AI Professional
Oracle
January 1, 2025 – Present
Smart Surveillance System for anti-theft, noise detection, visitor counting, and facial recognition using Python and OpenCV
IRSD 2024 Conference, Jamia Hamdard, India
January 1, 2024 – Present
Cultural Fit Analysis
The candidate demonstrates a strong cultural fit for an AI Engineer role, with a clear focus on AI/ML and GenAI technologies across all projects and educational pursuits. The diversity of projects, from multi-agent orchestrators to enterprise dashboards and image pipelines, shows adaptability and a broad interest in applying AI solutions. The continuous learning through certifications (DataCamp, Oracle, AWS) further indicates a proactive and growth-oriented mindset, aligning well with dynamic tech environments.
Soft Skills & Operational Fit
The candidate's project descriptions and experience highlight a results-oriented approach, focusing on quantifiable improvements (e.g., 25% reduction in hallucination, 60% reduction in manual reporting). This indicates a strong operational fit and a proactive mindset towards problem-solving and efficiency. The emphasis on maintaining production stability and automating workflows suggests a good understanding of operational health and reliability.