AI Research Developer

Tech stack: Python, SQL, GCP, WhyML, Bash, WhyML (Functional Programming Language)
Tools: JIRA/Confluence, PowerBI, Tableau, LangChain

Experienced at creating Agentic AI workflows using LangGraph, formal verification and prompt engineering. Previously applied machine learning algorithms for recommendation at VEChain, deploying using FastAPI and Docker to Google Cloud for 150k+ users. Automated 70% of data processing activities and uncovered 30+ fault patterns for 600 vehicles using unsupervised learning at Tata Technologies.

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Let's Connect

Ready to discuss your next AI project or explore collaboration opportunities? I'm always open to new challenges and innovative solutions.

📧 daniyalsid26@gmail.com 💼 LinkedIn Profile 🔗 GitHub Portfolio

About Me

I'm a developer specialising in implementing AI, Machine Learning and Data Science with 3+ years of experience building intelligent systems that solve real-world problems. My expertise spans machine learning, natural language processing and building agentic AI workflows with a focus on deploying scalable, production-ready solutions.

I have a strong background in Python including frameworks such as LangChain/LangGraph and PyTorch. I'm passionate about staying current with the latest developments in AI research including new methods of Prompt Engineering and AI deployment. I enjoy collaborating with cross-functional teams to bring AI innovations from concept to deployment.

Machine Learning

TensorFlow, PyTorch, Scikit-learn

Deep Learning

Neural Networks, CNN

NLP

Transformers, BERT, GPT

Agentic AI

LangChain, LangGraph

Cloud Platforms

AWS, Google Cloud

MLOps

Docker, CI/CD

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Featured Projects

Recommendation System (VEChain GreenCart, 2025)

  • Developed a recommendation system for a sustainable shopping application.
  • Extensively cleaned data noisy OCR data using NLP, used transformers to detect semantic similarity between items.
  • Deployed the model as a microservice to 150k users using FastAPI, Docker and Google Cloud Build, establishing a CI/CD pipeline.
  • Achieved 65% accuracy in recommending sustainable affiliate items to users.
Docker PyTorch Google Cloud Build FastAPI Regex Patterns
GitHub →

Formal Verification of Python Code using Why3 (UOG Centre for Cybersecurity CS2, 2025)

  • Built an automated LangGraph framework to formally verify Python code using LLM's.
  • Designed a graph architecture to call tools, parse output and implementing Human-in-the-Loop patterns.
  • Undertook extensive prompt engineering by creating system prompts and calling tools to generate relevant outputs.
LangGraph Prompt Engineering Agentic Memory ReAct Tool Calling Regex Patterns
View Project Video→ GitHub →