Machine Learning Engineer - LLM Models

Description :

Responsibilities :

- Design, train, and deploy deep learning models for NLP, forecasting, and anomaly detection tasks.

- Develop and fine-tune Large Language Models (LLMs) for domain-specific applications such as summarisation, Q& A, retrieval-augmented generation (RAG), and conversational agents.

- Apply time series modelling techniques (e. g., ARIMA, Prophet, LSTM, Transformer-based models) for forecasting, anomaly detection, and signal prediction.

- Build and optimise scalable data pipelines for model training, validation, and deployment.

- Collaborate with data engineers and software developers to integrate ML solutions into production environments.

- Evaluate and compare model performance using statistical metrics, and continuously iterate for improvement.

- Stay current with the latest research in deep learning, foundation models, and temporal modelling.

- Contribute to the development of internal tools, libraries, and best practices for ML experimentation and reproducibility.

Requirements :

- Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, or related field.

- 6+ years of hands-on experience building and deploying ML/DL models in production.

- Strong proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or JAX.

- Experience with transformer architectures (BERT, GPT, LLaMA, etc. ) and LLM fine-tuning (LoRA, PEFT, RLHF).

- Solid understanding of time series forecasting and signal processing techniques.

- Experience with MLOps tools and frameworks (e. g., MLflow, Kubeflow, Airflow, Docker, Kubernetes).

- Familiarity with cloud platforms (AWS, GCP, Azure) and GPU computing.

- Strong analytical, problem-solving, and communication skills.