Overview

Location: India, Remote-First

Responsibilities

• 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, working in Go to build inference services and integrate with gateway and endpoint pipelines.
• Optimize inference for performance and cost through techniques such as quantization, distillation, batching, and caching.
• Work closely with security researchers to turn emerging attack techniques into training data and detection logic.
• Build and maintain MLOps workflows covering reproducible training, model registries, monitoring, and safe model rollouts.
• Use AI-assisted development workflows to accelerate implementation, testing, debugging, and experimentation.

Qualifications

• Language: Fluent English required
• Industry: Cybersecurity / Enterprise SaaS / AI Security
• About The Opportunity
• Pragmatike is recruiting on behalf of a global enterprise cybersecurity company building a new generation of products to secure AI agents, LLM-powered applications, and the data they access.
• The Threat Detection team builds the models that determine, in real time, whether a prompt, response, tool call, or piece of data presents a security risk, including prompt injection, jailbreak attempts, sensitive data exposure, policy violations, and anomalous agent behavior.
• You'll own detection models end to end, from data and training through evaluation and production serving, with models running inline in Go-based gateway and endpoint services.
• We're looking for an ML Engineer who cares as much about latency, reliability, and production performance as model quality, and who is comfortable working across both Python and Go.
• 4+ years of experience building and deploying ML models in production, including NLP or LLM-based classification.
• Strong Python skills and experience with the modern ML stack, including 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.
• Strong evaluation discipline, including dataset design, metrics, error analysis, and adversarial testing.
• Fluent English with strong written and verbal communication skills.
• Comfortable using modern AI coding assistants such as Claude Code, Cursor, GitHub Copilot, Codex, or similar. This is a must-have.
• Strong ownership and the ability to work independently in a remote-first, distributed environment.
• Familiarity with adversarial ML and model robustness techniques.
• Previous experience in cybersecurity, security tooling, or trust & safety.
• Build real-time AI threat detection systems at the intersection of cybersecurity and agentic AI.
• Take models from research and experimentation all the way into low-latency production environments.
• Work on challenging problems spanning ML, LLM security, adversarial AI, and distributed systems.
• Own a critical detection workstream and influence how AI security threats are identified and mitigated.
• Collaborate with a highly technical, distributed team where AI is a core part of the development process.