ML Engineer – Threat Detection Models

Location: India, Remote-First
Employment: Full-Time
Start Date: November 2026
Experience: 4+ years
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 .
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.

What We're Looking For

-
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. This is a must-have.

- 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.

AI-First Engineering

AI is a core part of the engineering workflow on this team.

During the interview process, you'll be asked about how you use AI in real-world software development, including the tools you use, how you validate their output, and where AI has changed the way you work.

We're looking for engineers who use AI as a force multiplier for quality, productivity, and problem-solving , not s