AI Research Engineer - Applied AI

About PlexTrac

PlexTrac is a cybersecurity SaaS platform helping security teams streamline reporting, exposure management, and remediation workflows. Our platform is used by penetration testers, red teams, consultants, enterprises, and managed security providers to operationalize security findings and improve collaboration across technical and executive stakeholders.

We are a remote-first company headquartered in the United States with distributed team members across North America, Europe, and Asia. We are committed to ownership, transparency, practical problem-solving, and building products that customers genuinely rely on.

Why This Role Matters 

We are looking for an AI Research Engineer - Applied AI to build and ship the AI systems at the core of our security product. You will work across the full model lifecycle — from data pipelines and model training to deployment and production monitoring. You'll be at the forefront of our Agentic AI Offensive Security & Exposure Management Platform.

If you're into building cutting edge solutions that have a real impact on organizations' cybersecurity and enjoy working in a collaborative, cross functional start up environment, apply today!

Location: Remote — India only.

Responsibilities

Build, train, and evaluate machine learning models that detect security threats and unusual system behavior

Develop and maintain production AI features: prompt orchestration, retrieval-augmented generation (RAG), model serving, and observability

Work with raw security data — logs, network traffic, event streams — to build reliable training datasets

Build and maintain automated pipelines for model performance reporting and operational workflows

Design and maintain data ingestion and transformation services used by downstream AI systems

Monitor models in production, identify performance issues, and ship fixes

Test models for accuracy, bias, and reliability before they reach production

Work closely with security analysts to understand detection requirements and translate them into model improvements

Write clean, documented code that other engineers can read and use as a basis for implementation

Contribute to engineering standards for how the team develops and deploys models

Designing distributed training environments, optimizing computational efficiency, and managing GPU clusters.

Fine-tuning & Evaluation - Working with large language models (LLMs) and deep learning models using techniques like Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF).

Model Safety & Alignment -  Testing for vulnerabilities, mitigating biases, and ensuring models behave safely and predictably.

Qualifications

3+ years of software engineering experience with a focus on machine learning in production environments

Hands-on experience building and shipping ML models — not just training, but deploying and maintaining them

Strong Python skills and working knowledge of common ML libraries (scikit-learn, PyTorch, or TensorFlow)

Experience working with large, messy datasets — cleaning, labeling, and structuring data for model training

Familiarity with MLOps basics: versioning, monitoring, and retraining models in production

Ability to evaluate model performance clearly and explain trade-offs to non-technical teammates

Working knowledge of backend systems and API design

Nice to Have

Experience with security data — logs, SIEM output, network traffic, or endpoint telemetry

Background in anomaly detection, classification, or NLP applied to security use cases

Hands-on experience with LLM/RAG systems — performance tuning and reliability

Exposure to compliance-sensitive environments (SOC 2, ISO 27001, FIPS,  or FedRAMP)

Familiarity with responsible AI practices — bias auditing, explainability, and model documentation

Experience with Docker or Kubernetes for model deployment

Experience with cloud ML platforms (AWS SageMaker, GCP Vertex AI, or Azure ML)

Knowledge of data privacy regulations (GDPR, CCPA) and their impact on model training

Experience with Model Context Protocol (MCP) — building or integrating MCP servers and clients

Tech Stack

Modern AI engineering, cloud and hosted deployment environments, enterprise security workflows, scalable data systems, and modern SaaS infrastructure.

Work Style

We operate as a remote-first, distributed team with a strong asynchronous culture. We value thoughtful communication, autonomy, and collaboration, with core working hours that partially overlap with U.S. Eastern Time.

Employees are administered  through our EOR partner: Remote.

We’re committed to building an inclusive workplace where people from all backgrounds can thrive. We welcome applicants regardless of race, ethnicity, religion, gender identity, sexual orientation, age, disability, or background.

If you require accommodations during the interview process, please let us know: [email protected] 

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