Hungry, Humble, Honest, with Heart.

The Opportunity

We are reimagining observability at Nutanix with Panacea.ai, our next-gen AI-driven log and metrics analyzer. In version 1.0, we leveraged regex-based filters to surface anomalies. Now, we’re building Panacea.ai—powered by AI/ML, ModernBERT, and LLMs—to deliver intelligent, context-rich anomaly detection, automated root cause analysis (Auto-RCA), and continuous learning from user feedback.As a Staff Engineer (MTS-6), you will own the architecture and AI/ML systems that power both log and metrics analysis, enabling automated diagnostics and reducing triage time for QA failures, regression runs, and customer issues. You’ll also help define and drive the central AI charter at Nutanix, building reusable components, model infrastructure, and scalable ML services.

About the Team

The Panacea team has a passionate set of engineers across India and US office. We move fast, collaborate closely, and care deeply about quality and ownership. Our mission is to deliver AI/ML-powered developer productivity tools that solve real engineering and support pain points at scale.

Why Join Us
 

Build AI-first observability tools that redefine how engineers triage and troubleshoot.Own systems that reduce hours of manual work in engineering and SRE workflows.Collaborate with a tight-knit team of high-ownership engineers who are passionate about impact and innovation.Hybrid work model that supports flexibility and deep focus.Help shape the central AI charter at Nutanix and influence future AI products across the company.

Your Role

AI-Powered Observability Platform: Own the vision, architecture, and delivery of Panacea’s ML-based log and metrics analyzer that reduces triage time and improves engineering efficiency.AI/ML-powered Log Analyzer Tool: Use deep learning (e.g., ModernBERT) to represent log messages, detect anomalies, group patterns, and surface actionable insights to users.Metrics Anomaly Detection Engine: Build robust ML models to detect anomalies in time-series metrics like CPU, memory, disk I/O, network traffic, service health, and more—automatically identifying performance degradation or system regressions across distributed environments.Auto-RCA Engine: Combine log and metrics signals with graph-based correlation and LLM-powered summarization to automatically diagnose the root cause of system failures.Feedback Loop & Continuous Learning: Build infrastructure for incorporating user feedback to continuously retrain and improve anomaly detection systems.LLM Integration: Integrate LLMs for user queries, problem summarization, anomaly explanation, and contextual recommendations.Central AI Charter: Contribute to Nutanix’s foundational AI platform by defining shared tooling, datasets, governance, and reusable ML components across products.

Responsibilities
 

Architect and scale ML pipelines for real-time and batch-based anomaly detection in both logs and time-series metrics.Build and fine-tune ModernBERT and other transformer-based models for log understanding, anomaly classification, and summarization.Develop unsupervised and semi-supervised ML models for detecting anomalies in system metrics (CPU, memory, network throughput, latency, etc.).Implement correlation models to connect anomalies across logs and metrics to form a cohesive RCA narrative.Own the entire ML lifecycle: data ingestion, feature extraction, model training, evaluation, deployment, and monitoring.Build explainable AI systems that increase adoption and trust within engineering, QA, and support teams.Collaborate with cross-functional stakeholders (SRE, QA, Dev) to deeply understand pain points and translate them into intelligent tooling.Drive technical excellence through code and design reviews, mentoring, and setting engineering best practices.

What You Will Bring

Educational Background: B.Tech/M.Tech in Computer Science, Machine Learning, AI, or related fields.Experience: 12+ years of engineering experience , including designing , developing and deploying AI/ML systems at scale.ML Expertise:Strong in time-series anomaly detection, statistical modeling, supervised/unsupervised learning.Experience building ML models for metrics data (CPU, memory, IOPS, network, etc.) using models like Isolation Forest, Prophet, LSTM, or deep autoencoders.Expertise in NLP using ModernBERT, BERT, or log classification, clustering, and summarization.Experience with LLMs for downstream tasks like summarization, root cause reasoning, or intelligent Q&A.Engineering Skills: Strong Python background, hands-on with ML libraries (PyTorch, TensorFlow, Scikit-learn), time-series frameworks, and MLOps tools. Familiar with data pipelines and serving models.Observability Knowledge: Hands-on with logs, metrics, traces, and popular monitoring tools (e.g., Prometheus, Grafana, ELK).Leadership: Ability to independently drive projects from requirements to delivery, mentor junior engineers, and deliver business impact.

Work Arrangement

Hybrid: This role operates in a hybrid capacity, blending the benefits of remote work with the advantages of in-person collaboration. For most roles, that will mean coming into an office a minimum of 2 - 3 days per week, however certain roles and/or teams may require more frequent in-office presence. Additional team-specific guidance and norms will be provided by your manager.

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