ML Engineer – Threat Detection Models

About the role

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.

Key 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.
• Collaborate with security researchers to turn emerging attack techniques into training data and detection logic.
• Develop and maintain MLOps workflows covering reproducible training, model registries, monitoring, and safe rollouts.
• Use AI‑assisted development workflows to accelerate implementation, testing, debugging, and experimentation.

Required profile

• 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.
• 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, Kubernetes, and at least one major cloud provider.
• Fluent English with strong written and verbal communication skills.
• Comfortable using modern AI coding assistants such as Claude Code, Cursor, GitHub Copilot, Codex (must‑have).
• Strong ownership and ability to work independently in a remote‑first, distributed environment.

Required skills

• Python
• PyTorch
• Hugging Face Transformers
• scikit‑learn
• Go
• ONNX Runtime
• TorchServe
• Triton
• vLLM
• Docker
• Kubernetes
• GitHub Copilot
• Claude Code
• Cursor
• Codex