
Muhammad Daniyal
@muhammaddaniyal5
Backend Python Developer at AIGenics, optimizing self-hosted LLMs and building GPU-accelerated OCR services.
What I'm looking for
At AIGenics, I develop the product's FastAPI backend and run its on-premises platform and self-hosted LLMs across staging and production. I also optimized vLLM inference for more concurrent requests from limited GPU memory.
I built a Python OCR microservice that serves multiple GPU-accelerated engines through one API, processing pages in parallel and retrying failures. I also enforced JSON-schema structured outputs to prevent crashes from malformed model responses.
At Newsquawk, I engineered a headline monitoring system that brought news from financial, energy, and cryptocurrency sites to the news desk within seconds. I re-architected its Flask monolith into FastAPI microservices and reduced Twitter/X API usage by batching tracked accounts into advanced-search queries.
Experience
Work history, roles, and key accomplishments
Backend Python Developer
AIGenics
Mar 2026 - Present (7 months)
Primary developer of the product's FastAPI backend, securing access with RBAC, refresh-token reuse detection, single-session login and a full audit trail, and delivering real-time Socket.IO notifications on PostgreSQL. Built analytics APIs for management KPI, daily throughput and turnaround-time reporting, and optimized self-hosted LLM inference on vLLM.
Software Engineer, Python (Contract)
Newsquawk
Aug 2023 - Nov 2025 (2 years 3 months)
Engineered the headline monitoring system that put new headlines from 40+ financial, energy and cryptocurrency news sites, including Bloomberg and the Financial Times, in front of Newsquawk's news desk within seconds. Re-architected the Flask monolith into FastAPI microservices, isolating each news vertical on its own server.
Software Engineer
xLoop Digital Services
Nov 2022 - Jul 2023 (8 months)
Won the Top Champ award in a 40+ trainee data engineering bootcamp with Emeritus. Led a six-person capstone team that delivered a minute-by-minute room-occupancy prediction pipeline, streaming four sensors through Kafka Connect and PySpark into a pre-trained model across 8+ Docker Compose services.
Education
Degrees, certifications, and relevant coursework
PAF-KIET
Bachelor of Science, Computer Science
2018 - 2022
Grade: 3.65/4.0
Bachelor of Science in Computer Science with a CGPA of 3.65/4.0.
Microsoft
Microsoft Certified: Azure Fundamentals (AZ-900) and Azure Data Fundamentals (DP-900), Cloud and Data Fundamentals
Earned Microsoft Certified: Azure Fundamentals (AZ-900) and Azure Data Fundamentals (DP-900).
Availability
Location
Authorized to work in
Salary expectations
Job categories
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