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.

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AI Full-stack Engineering Intern

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:

• Backend

  : Python (FastAPI, HuggingFace Transformers, OpenCV), MySQL, RabbitMQ, GraphQL

• Front-End:

  React, TypeScript

• AI & Search:

  Elasticsearch, Redis, Vector DBs (Qdrant, ChromaDB)

• Cloud & DevOps:

  Docker, AWS, Azure, CI/CD, On-Prem Deployments

• Bonus

  : Kubernetes, GPU-based workloads (NVIDIA GPUs preferred)

Internship Details:

• Duration

  : 4-6 months

• Location

  : Onsite/Hybrid (min 3x a week) - Bengaluru (Jayanagar/Richmond Town)

• Stipend

  : Monetary compensation included.

• Additional Perks

  : Early access to product decisions, real-world deployment experience, high ownership.

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.

• Hands-On AI Exploration:

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.

• Evidence you can move beyond tutorials:

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

• Tooling & Framework Depth

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

• Experience running or optimising models on GPU/CPU edge devices; bonus points for on-device inference tricks (quantisation, pruning, TensorRT, ONNX, GGUF).
• Familiarity with vector databases (Qdrant, Chroma, Weaviate) and search frameworks (Elastic, OpenSearch).

Hardware-Aware Mindset:

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

• Basic exposure to the semiconductor / EDA landscape, PCB design, or HDLs (Verilog/VHDL).
• Past tinkering that bridges software with sensors, FPGAs, Raspberry Pi, Jetson, or lab equipment.
• Appreciation of constraints unique to on-prem or embedded deployments: latency, memory, thermals and how they influence architecture.

• MLOps & Dev-Infra Curiosity

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.

• Show-Don’t-Tell Proof

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.

• Contributions

  : big or small, to open-source projects in AI, DevOps, or hardware realms.

Personal Traits We Value:

• Relentless curiosity: you ask why and keep digging.
• Bias for rapid prototyping: build, test, iterate.
• System thinking: see the whole stack and optimize the right layer.
• Collaborative clarity: explain complex ideas simply and receive feedback constructively.
• Bring tangible evidence: code, designs, write-ups, documentation - showcasing how you learn and build. We’re excited to see what you’ve been tinkering with!

Hiring Process:

• GitHub Review
• Profile Shortlisting
• Technical Task/Assignment
• Deep Dive Interview
• Join the Team!