ML Engineer – Threat Detection Models

Pragmatike; Delhi; Remote; INR 1,000,000 - 1,700,000; Full time

Job summary

Pragmatike is seeking an ML Engineer to join a remote-first global team building threat detection models for AI security. You’ll own end-to-end ML workflows from data to production serving, with Go-based inference and Python tooling.

The role emphasizes latency, reliability, and security, and requires 4+ years of NLP/LLM experience, fluency in English, and strong backend skills. You’ll collaborate with security researchers, build MLOps pipelines, and optimize models for real-time detection of

Qualifications

• 4+ years of experience building and deploying ML models in production, including NLP or LLM-based classification.
• Strong Python skills and 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 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.

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.

Skills

Python

Go

NLP/LLM

Production ML

English communication

Tools

PyTorch

Hugging Face Transformers

scikit-learn

ONNX Runtime

Docker

Kubernetes

Job description

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 simply as an autocomplete tool.

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.

Pragmatike is committed to a fair, transparent, and inclusive recruitment process. We do not discriminate based on age, disability, gender, gender identity or expression, marital or civil partner status, pregnancy or maternity, race, religion or belief, sex, or sexual orientation.

In accordance with GDPR, your personal data will be processed lawfully, fairly, and securely and used solely for recruitment purposes, including sharing it with our client(s) for employment consideration.