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 :
• 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.

(ref : hirist.tech)