This job description outlines the key responsibilities, skills, and qualifications required for an LLM Engineer. We are seeking a highly skilled and motivated individual to join our team and contribute to the development and improvement of our large language models.
LLM Engineer Job Profile
An LLM Engineer specialises in developing, implementing, and optimising large language models to solve complex business problems. This role combines deep technical expertise in natural language processing with practical software engineering skills to build scalable AI solutions.
LLM Engineer Job Description
This role demands a strong understanding of machine learning principles and a proven ability to translate theoretical knowledge into practical applications. You will be responsible for designing and implementing efficient algorithms, optimising model performance, and ensuring the scalability and reliability of our LLM systems. You will also contribute to the development of new features and functionalities, continually improving the quality and capabilities of our models.
You will be working with a diverse team of engineers, researchers, and product managers, collaborating closely on projects and sharing your expertise. A commitment to continuous learning and staying up-to-date with the latest advancements in the field is essential.
Problem-solving is at the core of this role. You will be expected to identify and resolve complex technical challenges, often involving large datasets and intricate models. Your analytical skills and ability to communicate technical concepts clearly will be invaluable.
LLM Engineer Duties and Responsibilities
Model Development
- Design and implement large language model architectures for specific use cases
- Fine-tune pre-trained models using LoRA, QLoRA, and full parameter tuning techniques
- Develop training strategies for custom models on domain-specific datasets
- Implement prompt engineering techniques to optimise model outputs
- Create model evaluation pipelines to assess performance and safety
Technical Implementation
- Build MLOps infrastructure for model training, validation, and deployment at scale
- Develop high-performance APIs and microservices for model inference
- Optimise model performance through quantisation and compression techniques
- Design data processing pipelines for training datasets
- Implement distributed computing solutions for large-scale operations
Research and Quality Assurance
- Stay current with the latest LLM research and emerging AI technologies
- Conduct experiments with novel architectures and training methods
- Implement safety measures, including content filtering and bias detection
- Monitor model performance in production and implement improvements
- Collaborate with cross-functional teams on product integration
LLM Engineer Requirements and Qualifications
Education and Experience
- Master's degree in Computer Science, Machine Learning, or related field
- 3+ years of hands-on experience with large language models and NLP
- Proven track record of deploying ML models in production environments
Technical Skills
- Deep understanding of transformer architectures (GPT, BERT, T5, LLaMA)
- Extensive experience with PyTorch, TensorFlow, or JAX frameworks
- Proficiency in fine-tuning techniques and hyperparameter optimisation
- Expert-level Python programming with software engineering principles
- Experience with ML libraries (Hugging Face Transformers, Langchain)
- Knowledge of cloud platforms (AWS, Google Cloud, Azure) and ML services
- Familiarity with containerization (Docker, Kubernetes) and MLOps tools
- Understanding of GPU programming and distributed training
Preferred Qualifications
- Experience with multi-modal models and RLHF techniques
- Contributions to open-source projects or research publications
- Knowledge of vector databases and RAG systems
- Understanding of model interpretability and bias mitigation
Soft Skills
- Strong analytical thinking and problem-solving abilities
- Excellent communication skills for technical and business audiences
- Ability to work in collaborative, fast-paced environments
- Self-motivated with strong project management skills
