ML Engineer – Threat Detection Models

Pragmatike

. Design, train, and evaluate threat detectors using classifiers, embedding-based models, fine-tuned LLMs, and rule/ML hybrid approaches .

Posted 9/17/2026full-timeRemote • IndiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and deploying machine learning models, particularly in NLP and LLM-based classification, with a strong focus on model evaluation, optimization, and MLOps workflows. Proficient in Python and Go, with hands-on experience in using advanced tools and frameworks for model serving and performance tracking.

Highest-signal resume keywords

Machine Learning Model DeploymentNLP and LLM-Based ClassificationPython ProgrammingMLOps WorkflowsModel Evaluation and Optimization

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningNLPLLMModel EvaluationDataset DesignError AnalysisQuantizationFine-TuningBackend EngineeringModel Serving

Soft Skills

Strong Communication SkillsOwnershipIndependent Work

Tools & Technologies

PyTorchHugging Face TransformersScikit-learnDockerKubernetesONNX RuntimeTorchServeTritonVLLMAI Coding Assistants

Industry Keywords

MLOpsAdversarial TestingLow-Latency ServingCloud Provider ExperienceRemote Work

Tech Stack

Tools & technologies

CloudDockerKubernetesPythonPyTorchScikit-LearnGo

About the role

Key responsibilities & impact

Design, train, and evaluate threat detectors using classifiers, embedding-based models, fine-tuned LLMs, and rule/ML hybrid approaches
Build and maintain training and evaluation datasets, labeling workflows, and benchmark suites
Track model performance through precision/recall, error analysis, drift, and adversarial robustness
Serve models in production under strict latency requirements
Build inference services in Go and integrate them with gateway and endpoint pipelines
Optimize inference performance and cost through quantization, distillation, batching, and caching
Work with security researchers to turn emerging attack techniques into training data and detection logic
Build and maintain MLOps workflows for reproducible training, model registries, monitoring, and safe model rollouts
Use AI-assisted development workflows for implementation, testing, debugging, and experimentation

Requirements

What you’ll need

4+ years of experience building and deploying ML models in production, including NLP or LLM-based classification
Strong Python skills
Experience with PyTorch, Hugging Face Transformers, and scikit-learn
Hands-on experience fine-tuning transformer-based models
Solid Go skills, or strong backend engineering experience with the ability to become productive in Go quickly
Experience with low-latency model serving using ONNX Runtime, TorchServe, Triton, vLLM, or custom serving infrastructure
Strong evaluation discipline, including dataset design, metrics, error analysis, and adversarial testing
Experience with Docker and Kubernetes
Experience with at least one major cloud provider
Fluent English with strong written and verbal communication skills
Comfortable using AI coding assistants such as Claude Code, Cursor, GitHub Copilot, Codex, or similar; this is a must-have
Strong ownership and ability to work independently in a remote-first, distributed environment

Benefits

Comp & perks

Remote-first work arrangement
Opportunity to work with a global enterprise cybersecurity company
Work on real-time AI threat detection systems
Ownership of a critical detection workstream
Collaboration with a highly technical, distributed team
AI-assisted development workflows
Inclusive and transparent recruitment process