This article outlines the responsibilities and technical requirements for an intern tasked with fine-tuning DeepSeek-RI (Reward Instruct) or similar LLMs on defence-oriented datasets. It covers data engineering, model training and evaluation, inference optimization, secure Hugging Face deployment, and building Flutter-based query interfaces, plus the professional skills and operational expectations required for these workflows.
Core Responsibilities: Model Development and Data Engineering
Model fine-tuning and experimentation:
- Fine-tune DeepSeek-RI (Reward Instruct) or similar models on defence-oriented datasets, applying approaches such as LoRA, QLoRA or DeepSpeed where appropriate.
- Create and maintain training, preprocessing and evaluation scripts using PyTorch and the Transformers stack; use Datasets and Accelerate for scalable workflows.
- Implement evaluation pipelines focused on model reasoning, factuality and coherence to ensure outputs meet expected standards.
Data engineering and dataset management:
- Build, clean and structure datasets in JSONL/CSV formats from open-source defence intelligence sources (OSINT).
- Perform cleaning, preprocessing and normalization to prepare high-quality inputs for fine-tuning and evaluation.
- Manage model checkpoints, versioning and storage needs (25–50GB), ensuring reproducibility and traceability.
Performance and infrastructure considerations:
- Optimize inference performance across GPU and CPU environments to meet application latency and throughput targets.
- Work with Colab Pro / GPU infrastructure or local RTX/Jetson machines to run experiments and inference workloads.
- Maintain repository structure, logs and documentation to support development continuity and auditability.
Evaluation and quality assurance:
- Design and run evaluation pipelines specifically targeting reasoning quality, factual consistency and response coherence.
- Use standardized scripts and metrics implemented in the training and evaluation toolchain to track model improvements.
Deployment, Integration and Professional Requirements
Secure deployment and application integration:
- Deploy fine-tuned models to Hugging Face private repositories with secure access controls and manage model versions.
- Integrate API endpoints or the Hugging Face Inference API into mobile or web apps to serve model responses securely.
- Build a Flutter-based interface (Android/iOS/Web) for querying the fine-tuned LLM, including chat UI components and response viewing modules.
- Develop modules for secure authentication and token management, and ensure REST API consumption follows secure practices.
- Work with backend APIs implemented in FastAPI, Flask or Node to connect the front-end, authentication layers and model endpoints.
Professional skills, tooling and compliance:
- Bachelor’s degree in CS/AI/DS/Engineering (or equivalent experience); strong Python expertise in PyTorch, Datasets and Accelerate.
- Familiarity with Git and CI/CD workflows to manage code, experiments and deployments.
- Solid understanding of LLMs, Transformers and the Hugging Face stack, plus practical experience with fine-tuning methods (LoRA/QLoRA/DeepSpeed).
- Data engineering skills focused on cleaning, preprocessing and normalization of OSINT/defence datasets.
- Experience building Flutter apps and state management patterns (Provider, Bloc, GetX) to implement robust client-side behavior.
- Ability to write clear documentation (README, architecture diagrams) and maintain logs; awareness of AI ethics, safety and compliance in defence contexts is required.
Operational expectations:
- Manage model checkpoints and storage sizing (25–50GB) and coordinate use of Colab Pro or local GPU devices for experiments.
- Keep repository structure organized and document preprocessing, training and evaluation pipelines for team use.
Conclusion
In summary, the intern role combines fine-tuning DeepSeek-RI or similar models, rigorous dataset engineering from OSINT defence sources, evaluation for reasoning and factuality, and secure deployment to Hugging Face repositories. Candidates must integrate models into Flutter-based apps and backend APIs, optimize inference on GPU/CPU, manage checkpoints and storage, and maintain documentation, while adhering to AI ethics, safety and CI/CD practices.
