Job Description :

- Model adaptation & training

- Task-specific benchmarks focused on functional/executable correctness

- Standing up/optimizing infrastructure

- Reproducible pipelines (versioning, checkpoints, seeds, experiment tracking), structured generation.

Key Responsibilities :

- Design and execute end-to-end training and fine-tuning pipelines for LLMs to improve task-specific performance and domain accuracy.

- Implement advanced alignment techniques including DPO and GRPO to ensure model outputs are safe, reliable, and contextually relevant.

- Optimize model deployment and inference efficiency using techniques like LoRa and QLoRa to reduce computational overhead while maintaining high precision.

- Conduct continuous performance benchmarking and evaluation of CPT and fine-tuned models to ensure they meet rigorous quality standards before production release.

- Collaborate with cross-functional teams to integrate OLLama and Llama-based solutions into existing enterprise platforms, ensuring seamless scalability and performance.

Tech Stack :

- Python, Pytorch, Fine Tuning, Artificial Intelligence, GRPO, DPO, CPT, LoRa, QLoRa, OLLama, LLama