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: 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). (Optional Bonus) 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!