AI / ML, Data Science Engineer – Build the Future of Observability using Agentic AI
��  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
��  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.Strong Programming Skills  – Proficiency in Python  and ML-related libraries (TensorFlow, PyTorch, Scikit-learn, XGBoost, etc.), along with experience in distributed computing frameworks (Spark, Dask, Ray).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 technologieSomeone who is curious  and think differently  to challenge inefficiencies in the status quoA problem-solver mindset  who loves building from the ground upSkills Required
Machine Learning, Big Data, Sql, Deep Learning, Tensorflow, Cloud Computing, Pandas, Pytorch, XGBoost, Data Visualization, Python, Statistical Analysis