AI Engineer
Bengaluru
About Potpie
Potpie is building the foundation layer for spec-driven development in large, complex codebases. We focus on making software systems understandable, debuggable, and operable through structured context and AI agents. This is not about incremental code generation. This is about solving how software is built and maintained at scale.
Role
We are looking for an AI Engineer who can build, deploy, and scale machine learning systems for real-world applications. You will work across research, engineering, and product to turn models into reliable systems that deliver consistent performance in production. You will own the lifecycle from data and experimentation to deployment and iteration, working on problems involving large-scale data, model performance, and system reliability while collaborating closely with teams to deliver impactful, production-ready systems.
What you will do
Build, train, and deploy machine learning models across the product
Work on problems involving prediction, classification, and system understanding
Design data pipelines and workflows for training and inference
Improve model performance through experimentation, evaluation, and iteration
Collaborate with research, product, and engineering to ship usable systems
Monitor models in production and ensure reliability over time
Build infrastructure for training, evaluation, and deployment of ML systems
What we are looking for
2 to 8 years of experience
Strong programming skills with Python
Experience building and deploying ML models in production
Understanding of data pipelines, model evaluation, and performance metrics
Solid understanding of system design and backend fundamentals
Comfort working in ambiguity and iterating quickly
Clear thinking and communication
Tech stack (indicative, not restrictive)
Languages: Python, TypeScript
ML: PyTorch, TensorFlow, scikit-learn
Data: Pandas, Spark, feature pipelines
Infra: AWS or GCP
Backend: Python or Node.js services
Tools: ML pipelines, experiment tracking, model monitoring
Bonus
Experience with large-scale or distributed ML systems
Familiarity with LLMs, embeddings, or hybrid ML systems
Experience with data engineering or feature stores
Background in developer tools or infrastructure
Contributions to open source or public technical writing
Why this role
You will be working on real problems at the intersection of machine learning and software systems. The goal is not just to train models, but to build systems that make them reliable and useful at scale.
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