Principal Applied AI Researcher - Domain-Specific Models
New
IndiaFull-TimePrincipal
Salary not disclosed
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Job Details
- Experience
- 10+ years
- Required Skills
- PythonMachine LearningPyTorchNLPLLM
Requirements
- PhD or MSc in Computer Science, Artificial Intelligence, Machine Learning, NLP, or a related technical field.
- 10+ years of experience in AI/ML research, including at least 4 years working on LLM-based systems in production environments.
- Deep expertise across the complete AI model lifecycle, including pre-training, supervised fine-tuning, alignment, evaluation, and deployment.
- Strong hands-on experience with large-scale distributed training frameworks such as DeepSpeed, FSDP, or Megatron-LM.
- Advanced programming skills in Python and PyTorch, with the ability to contribute directly to complex technical implementations.
- Proven experience designing enterprise-grade evaluation methodologies beyond benchmark metrics, including robustness testing and failure analysis.
- Demonstrated leadership experience shaping AI research strategy, prioritizing investments, and guiding cross-functional technical teams.
- Strong understanding of data curation, data quality, and model performance optimization techniques.
- Experience with RLHF, DPO, reward modeling, or other post-training alignment methods is highly valued.
- Excellent communication, mentoring, and stakeholder management skills with the ability to influence both technical and executive audiences.
Responsibilities
- Define and lead the long-term strategy for domain-specific AI model development, including pre-training, fine-tuning, alignment, evaluation, and release readiness.
- Architect and optimize large-scale, agent-driven AI research infrastructure to accelerate experimentation, benchmarking, and model iteration cycles.
- Drive advanced research initiatives in model adaptation, data curation, reasoning improvement, reward modeling, and post-training optimization techniques.
- Lead the design of rigorous evaluation frameworks focused on robustness, explainability, enterprise readiness, and high-stakes deployment scenarios.
- Collaborate cross-functionally with engineering, product, and research teams to align AI capabilities with business priorities and customer needs.
- Influence platform-wide decisions around model lifecycle management, infrastructure scalability, and AI integration strategies.
- Mentor and develop senior researchers while fostering a culture of technical excellence, innovation, and scientific rigor.
- Contribute hands-on to research, experimentation, publications, patents, and externally visible technical leadership initiatives.
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