Full Stack Developer - AI Intern

Backend Video Pipelines

• Help design and implement backend pipelines for video generation and transformation.
• Integrate and orchestrate tools like FFmpeg and GStreamer for video encoding, decoding, compositing, and post-processing.
• Build services that chain together video AI models (open-source and third-party APIs) for tasks like generation, editing, and analysis.
  

Model Inference And AI Services

• Implement and optimize Python / PyTorch inference code for video and multimodal models.
• Contribute to batching, scheduling, and resource-efficient execution of model inference, including GPU-aware logic where applicable.
• Wrap models in robust APIs (e. g., FastAPI/Flask) that can be consumed by other services or frontends.
  

Agents, Tools, And Memory

• Work on agentic workflows using frameworks like LangGraph (and similar ecosystems).
• Implement and maintain agent tools for video processing, retrieval, and orchestration.
• Design and experiment with agent memory (short-term, long-term, episodic) using a combination of caches, vector stores, and graph databases.
  

Caching And Performance

• Implement caching layers for: Expensive model inferences, Frequently accessed assets (video segments, thumbnails), Agent context, and conversation state.
• Work with tools like Redis or similar technologies to reduce latency and cost.
• Contribute to performance profiling and basic optimizations (CPU/GPU usage, I/O, memory).
  

Graph Databases And Data Modeling

• Help design and implement graph database schemas for representing entities such as projects, scenes, clips, and workflows.
• Work with graph DBs (e. g., Neo4j / similar) to model relationships and power agent reasoning and retrieval.
• Write queries and small utilities to analyze and visualize graph data.
  

Cloud Infrastructure (GCP / AWS)

• Assist in deploying services to GCP and/or AWS, using managed services where appropriate.
• Work with: Compute (e. g., GCE/EC2 Cloud Run/Lambda), Storage (e. g., S3 GCS), Networking basics (load balancers, APIs, security groups/IAM).
• Contribute to simple infrastructure-as-code or configuration where needed.
  

Testing And Reliability

• Write unit, integration, and regression tests for: Python backend services, Model inference modules, Video processing pipelines.
• Help set up or extend CI/CD workflows (linting, tests, basic checks before deploy).
• Contribute to monitoring/logging hooks (e. g., logging inference times, error rates).
  

Requirements

• Currently pursuing a Bachelor's or Master's degree in Computer Science, Software Engineering, AI/ML, or a related field.
  

Core Programming

• Strong proficiency in Python (data structures, OOP, async basics).
• Comfortable working in a Linux development environment and using Git
  

AI / ML Fundamentals

• Solid understanding of machine learning / deep learning basics.
• Hands-on experience (coursework or projects) with PyTorch for training or inference.
• Ability to read and adapt open-source model code (e. g., diffusion/transformer models, vision models).
  

Backend Engineering

• Experience building basic backend APIs/services in Python (FastAPI, Flask, Django, or similar).
• Understanding of REST APIs, JSON, HTTP, and basic authentication concepts.
  

Media/video Processing

• Familiarity with FFmpeg (CLI usage, basic filters, transcoding) and/or GStreamer
• Understanding of core video concepts: codecs, containers, frame rate, resolution, and bitrates.
  

Cloud And DevOps Basics

• Exposure to at least one cloud platform (GCP or AWS can be via coursework or personal projects.
• Basic understanding of containers (Docker and how services are packaged and run.
  

Data And Storage

• Working knowledge of relational (SQL) and at least one NoSQL database.
• Interest in or exposure to graph databases and graph data modeling (nodes, edges, traversals).
  

Testing Mindset

• Familiarity with testing frameworks in Python (e. g., pytest, unittest).
• Ability to write simple unit tests and debug failing test cases.
  

Nice-to-Have (Big Plus)

• Prior project experience with:
• LangGraph, LangChain, or other agent frameworks.
• Building tool-using LLM agents (calling APIs/tools, RAG pipelines, memory).
• Graph DBs such as Neo4j, ArangoDB, TigerGraph, etc.
  

Experience With

• Redis or similar cache stores.
• Vector databases (e. g., Pinecone, Weaviate, Qdrant, Milvus) for retrieval/memory.
• GPU-accelerated workloads (CUDA basics, optimizing PyTorch inference).
• Contributions to open-source projects in video, ML, or agents.
• Portfolio of projects (GitHub, personal site) showcasing: Video/ML pipelines, Backend services, and Interesting AI demos.
  

Soft Skills

• Strong ownership and willingness to dive into unfamiliar stacks (video, agents, graph DBs).
• Curiosity about state-of-the-art video generation and AI agent systems.
• Ability to communicate clearly, document work, and collaborate with a small, fast-moving team.
• Comfortable iterating quickly, receiving feedback, and improving designs.