AI Research Engineer - Applied AI

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    "description": "<p><span style=\"font-family: Arial,sans-serif; font-size: 12pt; font-weight: bold\">About PlexTrac</span></p>\n<p><br></p>\n<p><span style=\"font-size: 12pt\">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.</span></p>\n<p><br></p>\n<p><span style=\"font-size: 12pt\">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.</span></p>\n<p><br></p>\n<p><span style=\"font-family: Arial,sans-serif; font-size: 12pt; font-weight: bold\">Why This Role Matters </span></p>\n<p><br></p>\n<p><span style=\"font-size: 12pt\">We are looking for an </span><span style=\"font-size: 12pt; font-weight: bold\">AI Research Engineer - Applied AI</span><span style=\"font-size: 12pt\"> 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. Y</span><span style=\"font-size: 12pt\">ou'll be at the forefront of our Agentic AI Offensive Security &amp; Exposure Management Platform.</span></p>\n<p><br></p>\n<p><span style=\"font-size: 12pt\">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!</span></p>\n<p><br></p>\n<p><span style=\"font-size: 12pt; font-weight: bold\">Location:</span><span style=\"font-size: 12pt\"> Remote — India only.</span></p>\n<p><br></p>\n<p><span style=\"font-size: 12pt; font-weight: bold\">Responsibilities</span></p>\n<ul>\n<li><span style=\"font-size: 12pt\">Build, train, and evaluate machine learning models that detect security threats and unusual system behavior</span></li>\n<li><span style=\"font-size: 12pt\">Develop and maintain production AI features: prompt orchestration, retrieval-augmented generation (RAG), model serving, and observability</span></li>\n<li><span style=\"font-size: 12pt\">Work with raw security data — logs, network traffic, event streams — to build reliable training datasets</span></li>\n<li><span style=\"font-size: 12pt\">Build and maintain automated pipelines for model performance reporting and operational workflows</span></li>\n<li><span style=\"font-size: 12pt\">Design and maintain data ingestion and transformation services used by downstream AI systems</span></li>\n<li><span style=\"font-size: 12pt\">Monitor models in production, identify performance issues, and ship fixes</span></li>\n<li><span style=\"font-size: 12pt\">Test models for accuracy, bias, and reliability before they reach production</span></li>\n<li><span style=\"font-size: 12pt\">Work closely with security analysts to understand detection requirements and translate them into model improvements</span></li>\n<li><span style=\"font-size: 12pt\">Write clean, documented code that other engineers can read and use as a basis for implementation</span></li>\n<li><span style=\"font-size: 12pt\">Contribute to engineering standards for how the team develops and deploys models</span></li>\n<li><span style=\"font-size: 12pt\">Designing distributed training environments, optimizing computational efficiency, and managing GPU clusters.</span></li>\n<li><span style=\"font-size: 12pt\">Fine-tuning &amp; 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).</span></li>\n<li><span style=\"font-size: 12pt\">Model Safety &amp; Alignment -  Testing for vulnerabilities, mitigating biases, and ensuring models behave safely and predictably.</span><br></li>\n</ul>\n<p><br></p>\n<p><span style=\"font-size: 12pt; font-weight: bold\">Qualifications</span></p>\n<ul>\n<li><span style=\"font-size: 12pt\">3+ years of software engineering experience with a focus on machine learning in production environments</span></li>\n<li><span style=\"font-size: 12pt\">Hands-on experience building and shipping ML models — not just training, but deploying and maintaining them</span></li>\n<li><span style=\"font-size: 12pt\">Strong Python skills and working knowledge of common ML libraries (scikit-learn, PyTorch, or TensorFlow)</span></li>\n<li><span style=\"font-size: 12pt\">Experience working with large, messy datasets — cleaning, labeling, and structuring data for model training</span></li>\n<li><span style=\"font-size: 12pt\">Familiarity with MLOps basics: versioning, monitoring, and retraining models in production</span></li>\n<li><span style=\"font-size: 12pt\">Ability to evaluate model performance clearly and explain trade-offs to non-technical teammates</span></li>\n<li><span style=\"font-size: 12pt\">Working knowledge of backend systems and API design</span><br><br></li>\n</ul>\n<p><span style=\"font-size: 12pt; font-weight: bold\">Nice to Have</span></p>\n<ul>\n<li><span style=\"font-size: 12pt\">Experience with security data — logs, SIEM output, network traffic, or endpoint telemetry</span></li>\n<li><span style=\"font-size: 12pt\">Background in anomaly detection, classification, or NLP applied to security use cases</span></li>\n<li><span style=\"font-size: 12pt\">Hands-on experience with LLM/RAG systems — performance tuning and reliability</span></li>\n<li><span style=\"font-size: 12pt\">Exposure to compliance-sensitive environments (SOC 2, ISO 27001, FIPS,  or FedRAMP)</span></li>\n<li><span style=\"font-size: 12pt\">Familiarity with responsible AI practices — bias auditing, explainability, and model documentation</span></li>\n<li><span style=\"font-size: 12pt\">Experience with Docker or Kubernetes for model deployment</span></li>\n<li><span style=\"font-size: 12pt\">Experience with cloud ML platforms (AWS SageMaker, GCP Vertex AI, or Azure ML)</span></li>\n<li><span style=\"font-size: 12pt\">Knowledge of data privacy regulations (GDPR, CCPA) and their impact on model training</span></li>\n<li><span style=\"font-size: 12pt\">Experience with Model Context Protocol (MCP) — building or integrating MCP servers and clients</span></li>\n</ul>\n<p><br></p>\n<p><span style=\"font-size: 12pt; font-weight: bold\">Tech Stack</span></p>\n<p><span style=\"font-size: 12pt\">Modern AI engineering, cloud and hosted deployment environments, enterprise security workflows, scalable data systems, and modern SaaS infrastructure.</span></p>\n<p><br></p>\n<p><span style=\"font-size: 12pt; font-weight: bold\">Work Style</span></p>\n<p><span style=\"font-size: 12pt\">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.</span></p>\n<p><br></p>\n<p><span style=\"font-size: 12pt\">Employees are administered  through our EOR partner: Remote.</span></p>\n<p><br></p>\n<p><span style=\"font-size: 12pt\">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.</span></p>\n<p><br></p>\n<p><span style=\"font-size: 12pt\">If you require accommodations during the interview process, please let us know: </span><span style=\"font-size: 12pt\"><a href=\"mailto:[email protected]\" target=\"_blank\" rel=\"noopener noreferrer\">[email protected]</a></span><span style=\"font-size: 12pt\"> </span><br></p>\n<p><br></p>\n<p><span style=\"font-family: Arial,sans-serif; font-size: 12pt\">#LI-Remote</span></p>",
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