Machine Learning Engineer - AI Applications

Responsibilities

• Develop and program integrated software solutions for the development, deployment, and lifecycle of machine learning and generative AI models.
• Train and validate models using techniques such as prompt engineering and fine-tuning of Large Language Models (LLMs).
• Design, develop, and maintain scalable ML and GenAI pipelines, including RAG, embeddings, and vector databases.
• Deploy models to production environments, ensuring scalability, reliability, and efficiency through containerization and orchestration.
• Optimize hyperparameters and monitor deployed models to ensure high performance and adaptation to changing data.
• Partner with research and engineering teams to enhance automated model training and GenAI capabilities.

Requirements

• Bachelor’s or Graduate Degree in Computer Science, Statistics, Mathematics, Data Science, Artificial Intelligence, or a related discipline.
• 4–7 years of experience in software development, machine learning, or generative AI (candidates with advanced degrees may have 3–5 years of experience).
• Proven hands-on experience with Large Language Models (LLMs) and multimodal GenAI systems.
• Proficiency in Python and machine learning libraries such as PyTorch, TensorFlow, or Scikit-learn.
• Strong knowledge of Retrieval-Augmented Generation (RAG) and vector databases (e.g., FAISS, ElasticSearch).
• Experience with model deployment in production environments and familiarity with cloud platforms like Azure.

Preferred Qualifications

• Experience with LangChain and Hugging Face Transformers.
• Familiarity with OpenAI API and prompt engineering techniques.
• Knowledge of CI/CD processes and containerization for ML services.
• Experience working within Agile methodologies.

About the Company

HP is a global technology leader dedicated to creating innovation that drives business value and reshapes how the world works. We foster a culture of respect, diversity, and collective achievement, empowering our teams to build the future of work.