AI Engineer

• Develop and fine-tune Generative AI models (e.g., LLMs, diffusion models).
• Design and implement machine learning models for classification, regression, clustering, and recommendation tasks.
• Build and maintain scalable AI pipelines for data ingestion, training, evaluation, and deployment.
• Collaborate with cross-functional teams to understand business needs and translate them into AI solutions.
• Ensure model performance, fairness, and explainability through rigorous testing and validation.
• Deploy models to production using MLOps tools and monitor their performance over time.
• Stay current with the latest research and trends in AI/ML and GenAI and evaluate their applicability to business problems.
• Document models, experiments, and workflows for reproducibility and knowledge sharing.

 Technical Skill Set

  Cloud & Infrastructure (AWS)

• Amazon SageMaker – Model training, tuning, deployment, and MLOps.
• Amazon Bedrock – Serverless GenAI model access and orchestration.
• SageMaker JumpStart – pre-trained models and GenAI templates.
• Prompt engineering and fine-tuning of LLMs using SageMaker or Bedrock.

   Programming & Scripting

• Python – Primary language for AI/ML development, data processing, and automation.