Observe.AI
About this role
Observe.AI builds an AI agents platform for customer experience, used by brands including DoorDash and Signify Health to deploy voice agents, chat agents and copilots at enterprise scale. This role builds the backend those agents run on. You take the full application lifecycle from design through coding, testing, deployment and debugging, translate the product roadmap into a technical one alongside product and design leaders, and build reusable code, frameworks and libraries that keep development consistent. A significant part of the work is performance and scale: optimising for speed and reliability, addressing bottlenecks proactively, designing scalable data storage and building efficient pipelines that process large volumes accurately. Security and data protection are called out explicitly, which follows from handling enterprise customer conversations. It suits a backend engineer who wants to work behind production AI systems rather than on the models themselves.
Who this is for
What the posting asks for: 5 to 9 years of experience building large scale products.
The real day to day: contributing to building and deploying AI agents for customer service in the form of voice agents, chat agents and copilots at enterprise scale. Owning the full application lifecycle through design, coding, testing, deployment and debugging with a high quality bar. Working with product and design to translate the product roadmap into a clear technical roadmap. Developing reusable code, frameworks and libraries to speed up development and keep projects consistent. Optimising applications for speed, scalability and reliability by proactively addressing bottlenecks and performance issues. Implementing security measures and data protection practices to safeguard applications and sensitive customer data. Designing and managing scalable data storage that stays efficient, reliable and maintainable. Building and scaling efficient data pipelines that process large volumes accurately. Reviewing others' work as part of the team's quality process.
Location and setup: Bengaluru. The posting does not state office days.
Honest fit guidance: this is backend platform engineering supporting AI products, not machine learning engineering, so the daily work is distributed systems, data pipelines and API design rather than models and evaluation. If you want to move closer to AI without retraining as an ML engineer, that distinction works in your favour. Observe.AI describes a culture that prizes speed and ownership, which usually means less process and more direct responsibility. Note Observe.AI also has a Senior ML Engineer NLP role on today's board at 3 or more years, which is the model side of the same product, so pick according to which half of the system you want to own.
The real day to day: contributing to building and deploying AI agents for customer service in the form of voice agents, chat agents and copilots at enterprise scale. Owning the full application lifecycle through design, coding, testing, deployment and debugging with a high quality bar. Working with product and design to translate the product roadmap into a clear technical roadmap. Developing reusable code, frameworks and libraries to speed up development and keep projects consistent. Optimising applications for speed, scalability and reliability by proactively addressing bottlenecks and performance issues. Implementing security measures and data protection practices to safeguard applications and sensitive customer data. Designing and managing scalable data storage that stays efficient, reliable and maintainable. Building and scaling efficient data pipelines that process large volumes accurately. Reviewing others' work as part of the team's quality process.
Location and setup: Bengaluru. The posting does not state office days.
Honest fit guidance: this is backend platform engineering supporting AI products, not machine learning engineering, so the daily work is distributed systems, data pipelines and API design rather than models and evaluation. If you want to move closer to AI without retraining as an ML engineer, that distinction works in your favour. Observe.AI describes a culture that prizes speed and ownership, which usually means less process and more direct responsibility. Note Observe.AI also has a Senior ML Engineer NLP role on today's board at 3 or more years, which is the model side of the same product, so pick according to which half of the system you want to own.
Apply on company site
Opens www.observe.ai, the employer's own application page. Applying is always free.