[Hiring] ML Engineer @Pragmatike

Role Description

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
• 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.

What You’ll Do

• 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

• 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 technologies such as 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, or similar.
• Strong ownership and the ability to work independently in a remote-first, distributed environment.

Nice to Have

• Experience building detection systems for prompt injection, jailbreaks, or content safety.
• Experience with DLP or sensitive-data classification, including PII, secrets, or source code.
• Familiarity with adversarial ML and model robustness techniques.
• Previous experience in cybersecurity, security tooling, or trust & safety.
• Experience introducing AI-assisted or agentic development workflows across engineering teams.

Why Join

• 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.