Experience: 

1/4/6+ years in production full stack apps.


‍Responsibilities:
‍Junior (1+ year)

Build and maintain features across frontend and backend under guidance from senior developers.
Write clean, well-structured code following established patterns and conventions.
Fix bugs, write basic tests, and participate in code reviews as a reviewer and author.
Learn the codebase, ask good questions, and gradually take ownership of smaller modules.

Mid-Level (4+ years)

Own full features end-to-end , from database schema and API design to UI implementation.
Integrate third-party services, design RESTful/GraphQL APIs, and manage data modeling.
Write meaningful tests, review PRs thoroughly, and mentor junior developers.
Contribute to architectural discussions, sprint planning, and technical decision-making.

Senior (6+ years)

Drive architecture decisions across the stack — define patterns, enforce standards, and manage tech debt.
Lead complex cross-team initiatives, break down ambiguous requirements into executable plans.
Own performance, scalability, and reliability of critical system components.
Mentor the team, shape engineering culture, and act as a bridge between product and engineering.

Nice to have (all levels): SSR/SSG experience, containerization (Docker), CI/CD pipelines, infrastructure basics (cloud, monitoring), Storybook, and experience with message queues or background job processing.

Experience: 

1/4/6+ years in production AI.


‍Responsibilities:
‍Intern

Assist with data cleaning, labeling, and preprocessing under direct supervision.
Run existing training scripts, log experiments, and document results.
Learn core ML concepts including model evaluation, overfitting, and common architectures.
Build small proof-of-concept notebooks and present findings to the team.

Junior (1+ year)

Train, evaluate, and fine-tune models using established frameworks (PyTorch, TensorFlow, HuggingFace).
Build and maintain data pipelines for feature extraction and model input preparation.
Write clean, reproducible experiment code with proper versioning and logging (MLflow, W&B).
Integrate trained models into backend services via REST APIs or batch inference jobs.

Mid-Level (4+ years)

Design end-to-end ML solutions, from problem framing and dataset strategy to deployment and monitoring.
Optimize model performance, latency, and cost across training and inference.
Build and improve internal tooling for experiment tracking, evaluation, and model serving.
Evaluate new techniques (LoRA, RAG, prompt engineering) and recommend adoption where appropriate.
Mentor juniors, review ML code and experiment design, and contribute to technical roadmaps.

Senior (6+ years)

Define the ML strategy, identifying where AI adds real business value and where it doesn't.
Architect production ML systems with focus on scalability, observability, and reliability.
Lead research-to-production cycles for complex problems (NLP, vision, recommendation, generative AI).
Own model governance including bias auditing, data privacy, and responsible AI practices.
Shape the team's technical direction, hiring bar, and engineering culture around ML best practices.

Nice to have (all levels): LLM/prompt engineering experience, vector databases, cloud ML services (AWS SageMaker, GCP Vertex), containerization, familiarity with ONNX/TensorRT for optimization, and experience with real-time inference or streaming pipelines.