- SQL
- Bash
Programming Languages
The core toolkit: Python for models and services, TypeScript across the web, Go where throughput matters.
python, typescript, javascript, go
@ Ahmed AfzalSenior AI Engineer at Sagaris
Senior AI & ML Engineer building and training end-to-end AI systems at scale: LLMs, RAG pipelines, agentic AI, and computer vision, from model to production.
RésuméSkillsSkillsthatthatfuelfuelmymypassionpassion
- Hugging Face
- NumPy
- Pandas
- Ultralytics
- PandasAI
ML Frameworks & Libraries
Training, evaluating, and fine-tuning models, from classical estimators to multimodal transformers.
pytorch, tensorflow, scikitlearn, opencv
- Agentic AI
- RAG Pipelines
- MCP
- A2A
- Generative AI
- NLP
- Computer Vision
- Deepfake Detection
AI & Agentic Systems
LLM systems that do real work: retrieval pipelines, tool-using agents, and multi-agent orchestration over MCP.
langchain, openai
- Model Training & Evaluation
- Fine-tuning
- Hyperparameter Tuning
- Tensor & Pipeline Parallelism
- Transformers
- Neural Networks
- CNN
- RNN
- MLOps
Core ML
The fundamentals underneath the frameworks: how models get trained, scaled across GPUs, and kept honest in production.
- Express
- WebSockets
- Sharding
- Parallel Processing
Backend Development
Low-latency inference services and the APIs around them, built to stay up under real clinical and sales workloads.
nodejs, fastapi, flask, django
- Shadcn
Frontend & Web
Interfaces for AI products: viewers, dashboards, and the tools clinicians and operators actually touch.
react, nextjs, tailwindcss, typescript
- Pinecone
- AWS S3
- Cloudflare R2
Databases & Storage
Relational, document, and vector stores, chosen for the retrieval pattern rather than out of habit.
postgresql, mongodb, mysql, supabase
- Stripe
- CI/CD
- GitHub Actions
- ComfyUI
- CrewAI
Tools & Infrastructure
Containers, pipelines, and the boring machinery that turns a trained model into something on-call can trust.
docker, kubernetes, amazonwebservices, git, github, jenkins, linux, n8n