AI Research Engineer – Applied AI

This is a fully remote position, open to applicants in India.

📋 Description

• Develop, train, and assess machine learning models that identify security threats and abnormal system activities.

• Create and uphold production-level AI features, including prompt orchestration, retrieval-augmented generation (RAG), model serving, and observability.

• Utilize raw security data — such as logs, network traffic, and event streams — to construct dependable training datasets.

• Establish and sustain automated pipelines for reporting model performance and managing operational workflows.

• Design and oversee data ingestion and transformation services utilized by subsequent AI systems.

• Supervise models in production, detect performance challenges, and implement solutions.

• Evaluate models for accuracy, bias, and reliability prior to their deployment in production.

• Collaborate closely with security analysts to comprehend detection needs and translate them into enhancements for models.

• Produce clean, well-documented code that is accessible for other engineers to read and utilize as a foundation for implementation.

• Contribute to engineering best practices regarding the development and deployment of models within the team.

• Design distributed training environments, enhance computational efficiency, and manage GPU clusters.

• Fine-tuning & Evaluation - Engage with large language models (LLMs) and deep learning models using methods such as Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF).

• Model Safety & Alignment - Assess for vulnerabilities, mitigate biases, and guarantee that models operate safely and predictably.

⛳️ Requirements

• Over 3 years of experience in software engineering, with a particular emphasis on machine learning in production settings.

• Practical experience in building and deploying ML models — focusing on both training and ongoing maintenance.

• Proficient in Python and knowledgeable about common ML libraries, such as scikit-learn, PyTorch, or TensorFlow.

• Experience handling large, complex datasets — including cleaning, labeling, and structuring data for model training.

• Familiarity with MLOps fundamentals: managing versioning, monitoring, and retraining models in production.

• Ability to assess model performance clearly and articulate trade-offs to non-technical colleagues.

• Understanding of backend systems and API design.

🏝️ Benefits

• Health insurance

• Professional development opportunities