AI Full-Stack Engineering Intern

At Electronix.AI, we’re building tools for the engineers of tomorrow, tools that understand, automate, and accelerate the hardware development cycle.

Website : Electronix AI | Accelerate Hardware Design Decisions

We are now looking for an AI Full-stack Engineering Intern to join our team!

This is your opportunity to be part of a fast-paced startup building AI-powered tools for the hardware world. You'll work with people who value autonomy, rapid prototyping, and deep problem-solving, and you'll help shape systems used in the field, not just in the cloud.

What You’ll Be Doing (Responsibilities) :

• Develop and optimize backend services using Python (FastAPI, HuggingFace Transformers, OpenCV).
• Design and deploy APIs for AI-powered automation, video analytics, and search applications.
• Develop and deploy robust on-premises solutions that integrate seamlessly with existing infrastructure.
• Implement and manage DevOps pipelines, ensuring continuous integration and deployment workflows across cloud and on-prem environments.
• Ensure scalability, security, and reliability across infrastructure and applications.
• Work with databases and caching layers like MySQL, Elasticsearch, Redis, and Vector DBs (Qdrant, ChromaDB, etc.).
• Develop frontend components using TypeScript, React, and GraphQL to create intuitive user experiences.
• Troubleshoot and resolve complex system performance issues, particularly for on-prem deployments and high-performance computing environments. (Nice to Have)
• Experience with Kubernetes for container orchestration

What we need to see :

Internship Details :

Good to Have :

These are extras that help us spot naturally curious, hands-on builders. None are hard requirements especially the hardware items, which are purely optional bonuses.

Personal / academic projects showing end-to-end use of modern AI stacks—e.g., fine-tuning or serving models with Hugging Face Transformers, building RAG pipelines, or experimenting with OpenAI, Gemini, or Ollama APIs.

custom data pipelines, evaluation scripts, or deployment artefacts that solve real problems.

Comfort with open-source LLM toolchains such as LangChain, LlamaIndex, FastEmbed, or Haystack.

Optional Bonus) Hardware-Aware Mindset :

Purely a plus, great if you have it, absolutely fine if you don’t.

Initial exposure to MLOps concepts : experiment tracking (Weights & Biases, MLflow), model registry, automated evaluation. Comfort scripting IaC with Terraform / Ansible or writing GitHub Actions to ship prototypes rapidly.

An active GitHub with readable READMEs, issues, and commit history reflecting iterative learning.

Blog posts, lightning talks, or demo videos explaining challenges, trade-offs, and learnings. Clear communication counts.

Personal Traits We Value :

Hiring Process :