LLM Engineer

Roles and Responsibilities :

• • Lead the design, development, training, and deployment of ML, Deep Learning, and Generative AI models, with a strong foundation in Data Science/Machine Learning.
  • Develop solutions across prediction, classification, recommendation, forecasting, optimization, NLP, and GenAI use cases beyond traditional LLM applications.
  • Perform model fine-tuning and optimization using techniques such as LoRA, PEFT, RLHF, DPO, prompt optimization, quantization, and model distillation.
  • Build RAG and Agentic AI solutions, including vector stores, retrievers, and frameworks such as LangGraph, AutoGen, CrewAI, or OpenAI Agents.
  • Build and manage end-to-end MLOps pipelines covering experimentation, CI/CD, model deployment, monitoring, evaluation, and lifecycle management using platforms such as MLflow, SageMaker, Databricks, or Kubeflow.
  • Design scalable AI solutions on AWS, with basic architecture understanding and hands-on experience deploying production-grade AI/ML systems.
  • Work with structured/unstructured data using Python, Pandas, NumPy, Scikit-Learn, PyTorch, TensorFlow, and XGBoost, while collaborating with business stakeholders to deliver measurable outcomes.
  
  

Job Requirements :

• 7-14 years of experience in ML Engineering or a related field.
• Strong expertise in deep learning, machine learning engineering, and generative AI.
• Proficiency in Python programming language with experience working on AWS SageMaker