AI/ML, Data Science Engineer – Build the Future of Observability using Agentic AI

Are you passionate about building the next-generation Observability powered by AI agents? Are you excited by the challenge of working on Time-Series and Causal Analysis at scale? Are you game to innovate and build time-series LLMs? Do you thrive in a fast-paced startup environment where you can make a real impact? We are redefining Observability with Agentic AI, and we’re looking for a Platform Engineer to help shape this future.

Why Join Us?

Innovate at the Forefront of AI – Work with a dynamic team developing next-gen AI driven infrastructure. Your contributions will directly influence the next generation Observability

Ownership & Impact – As an early member of our AI/ML team, you’ll have an hands-on role in applying time-series and causal analytics to solve complex observability problems. You will be working on building model evaluation frameworks and innovating on building time-series LLMs.

Startup Agility, Big Challenges – Enjoy the best of both worlds: the agility and innovation of a startup, combined with the challenge of finding deep hidden behavior patterns and causal relationships using traditional and new state of the art methods.

Rock Star Team - Opportunity to work closely with a seasoned and talented founding team, who have built multiple billion dollar products from ground up. Get a front seat to see how things are built from scratch in deep-technology startups.

Tech Stack & Tools

• Hands on experience with time-series algorithms, causal analytics, building model evaluations at scale and deploying in production
• Built production ready AI agents, experience in multi-model LLM fine-tuning 
• Deep understanding of MLOps pipelines
• Python, PyTorch, Time Series Analysis libraries, Causal Analysis libraries

What We’re Looking For

• Extensive Experience in Time-Series Analysis – Proven ability to build, deploy, and optimize time-series models for forecasting, anomaly detection, and decision-making at scale.

• Expertise in Causal Analytics – Strong understanding of causal inference techniques to drive insights and actionable recommendations from large datasets.

• Hands-on with Large-Scale Data – Experience working with high-dimensional, large-scale structured and unstructured datasets in production.

• MLOps & Model Lifecycle Management – Deep understanding of MLOps best practices, including model training, evaluation, deployment, monitoring, and continuous improvement.

• LLM Fine-Tuning & Distillation – Experience fine-tuning, distilling, or optimizing multi-modal LLMs for efficiency and accuracy in production applications.

• Production-Grade ML Systems – Track record of developing robust, scalable ML pipelines and integrating models into the production environment.

* Passion for AI and leveraging cutting-edge technologie 

* Someone who is curious and think differently to challenge inefficiencies in the status quo

* A problem-solver mindset who loves building from the ground up

Be Part of Something Big

Here, you won’t just be an employee – you’ll be a key player in defining the future of Observability. If you’re looking for a high-impact, high-growth opportunity, we’d love to talk to you!