Research Engineer - AI Security
First seen by Alion on Sep 5, 2026.
Role : Research Engineer - AI Security
Location : Bengaluru, India
Function : AI Security - Technology & Innovation Centre (TIC)
Employment Type : Full-time
Role Purpose :
The Research Engineer - AI Security is responsible for building, automating, and scaling experimental infrastructure that enables rigorous, reproducible AI security research. The role focuses on experiment automation, benchmarking, and research-to-solution engineering to support secure, compliant, and trustworthy AI systems.
Key Responsibilities :
- Design and implement reproducible experiment pipelines for AI security research.
- Build automation frameworks for benchmarking, ablation studies, and large-scale model evaluations.
- Develop experimental benches for model fingerprinting, mechanistic analysis, unlearning, and provenance.
- Translate research prototypes into engineering-grade proof-of-concepts and reusable components.
- Collaborate with applied researchers, backend engineers, and MLOps teams to enable deployment-ready solutions.
- Maintain experiment tracking, versioning, and validation to ensure reliability and auditability.
- Contribute to technical documentation, evaluation reports, and research artefacts.
Required Qualifications & Experience :
Education :
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related engineering discipline.
Experience :
- 3 - 6+ years of experience in research engineering, ML engineering, or experimentation-heavy roles.
- Hands-on experience supporting AI/ML research with scalable engineering systems.
Required Technical Skills :
- Strong proficiency in Python and deep learning frameworks such as PyTorch.
- Experience working with large language models, fine-tuning workflows, and adapter-based methods (e.g., LoRA).
- Understanding of model internals including parameters, activations, embeddings, and output distributions.
- Experience with experiment tracking, benchmarking, and reproducible research workflows.
- Familiarity with AI security, ML robustness, model analysis, or privacy-related techniques is desirable.
Success Indicators :
- Delivery of reliable, repeatable experimental infrastructure supporting AI security research.
- Effective enablement of applied researchers through automation and tooling.
- Successful transition of research outputs into deployable proof-of-concepts or platform components
Skills
R&D - Security, R&D, Artificial Intelligence, Machine Learning, Security, Security Architect - AI, Deep Learning, PyTorch, LLM, MLOps