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Staff Engineer/Tech Lead – AI/ML [ Natural Language Processing

10 - 17 years

30 - 40 Lacs

Bengaluru

Posted:6 days ago| Platform: Naukri logo

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Skills Required

python natural language processing unsupervised learning tensorflow statistical modeling cnn scikit-learn neural networks networking bert machine learning artificial intelligence sql deep learning data science grafana pytorch troubleshooting

Work Mode

Hybrid

Job Type

Full Time

Job Description

Staff Engineer/Tech Lead AI/ML [ Natural Language Processing, Transformers, Gen AI, LLM, Neural Networks]

The Opportunity

Staff Engineer (MTS-6)

About the Team

Panacea

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 Panaceas 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 moreautomatically 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 Nutanixs 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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