<p><b style="background-color: inherit;">What You'll Work On :</b><br/><br/>- Develop state-of-the-art time series models for anomaly detection and forecasting in observability data.<br/><br/>- Design a root cause analysis system using LLMs, causal analysis, machine learning and anomaly detection algorithms.<br/><br/>- Develop Large Language Models for time series analysis<br/><br/>- Create highly scalable ML pipelines for real-time monitoring and alerting.<br/><br/>- Build and maintain ML Ops workflows for model deployment, evaluation, monitoring, and updates.<br/><br/>- Build frameworks to evaluate AI agents.<br/><br/>- Handle large datasets using Python and its ML ecosystem (e.g., NumPy, Pandas, Scikit-Learn, TensorFlow, PyTorch, Statsmodels).<br/><br/>- Use Bayesian methods, Granger causality, counterfactual analysis, and other techniques to derive meaningful system insights.<br/><br/>- Collaborate with other teams to deploy ML-driven observability solutions in production.<br/><br/></p><p><b>What We're Looking For :</b></p><p><b><br/></b></p><p>- 5+ years of hands-on experience in Machine Learning, Time Series Analysis, and Causal Analytics.</p><p><br/></p><p>- Bachelors degree in Computer Science, Mathematics or Statistics.
Masters or PhD is a plus</p><p><br/></p><p>- Strong proficiency in Python and libraries like Scikit-Learn, TensorFlow, PyTorch, Statsmodels, Prophet, or similar.</p><p><br/></p><p>- Deep understanding of time-series modeling, forecasting, and anomaly detection techniques.</p><p><br/></p><p>- Expertise in causal inference, Bayesian statistics, and causal graph modeling.</p><p><br/></p><p>- Experience in ML Ops, model evaluation, and deployment in production environments.</p><p><br/></p><p>- Working knowledge of databases and data processing frameworks (SQL, Spark, Dask, etc.).</p><p><br/></p><p>- Experience in observability, monitoring, or AI-driven system diagnostics is a big plus.</p><p><br/></p><p>- Background in AI Agent evaluation and optimization is a plus.</p><p><br/></p><p>- Working with LLMs, fine-tuning LLMs, LLM Ops, LLM Agents is a big plus</p><p><br/></p><p><b>Our Values :</b></p><p><br/></p><p>- Loyalty & Long-term Commitment - We invest in people who invest in us.</p><p><br/></p><p>- Opinionated yet Open-Minded - We value strong perspectives but encourage constructive discussions.</p><p><br/></p><p>- Passion - We seek individuals who are passionate about their craft.</p><p><br/></p><p>- Humility & Integrity - Honest, transparent, and accountable team members are key.</p><p><br/></p><p>- Adaptability & Self-Sufficiency - Ability to thrive in a fast-paced and evolving environment.</p><p><br/></p><p>- Build Fast and Break Fast - We believe in rapid iteration and learning from failures.</p><br/><b>What You'll Work On:</b><br/><br/>You will be instrumental in building the next-generation Observability platform for automated Root Cause Analysis using LLMs, Machine Learning algorithms.
You will be innovating on building LLMs for time series analysis.
You'll have the opportunity to work with an experienced team, gain deep insights into how startups are built, and be at the forefront of disruptive innovation in Observability.<br/><br/><b>Comp</b> - upto 2Cr+<p></p> (ref:hirist.tech)