LLM EngineerRole Description

The LLM Engineer is responsible for designing, developing, and optimizing applications powered by large language models (LLMs) to support innovative products and business solutions. This role works closely with software engineers, data scientists, product managers, and cross-functional teams to build scalable, reliable, and high-performing AI systems that deliver meaningful user experiences.

Key responsibilities include developing LLM-based applications, integrating foundation models into software products, designing prompt engineering strategies, and optimizing model performance for accuracy, efficiency, and reliability. The LLM Engineer collaborates with technical teams to define solution architectures, evaluate model capabilities, and implement AI-powered features that align with business objectives and user needs.

The role involves building retrieval-augmented generation (RAG) pipelines, integrating vector databases, developing AI workflows, and connecting language models with external APIs, enterprise systems, and knowledge bases. The LLM Engineer contributes to model evaluation, prompt optimization, response quality assessment, and performance monitoring while ensuring scalability, maintainability, and responsible AI practices throughout the development lifecycle.

Additional responsibilities include developing automated testing strategies for AI applications, monitoring system performance, improving inference efficiency, maintaining technical documentation, and identifying opportunities to enhance AI functionality through emerging technologies and best practices. The position requires continuous learning to stay current with advancements in generative AI, natural language processing, and machine learning frameworks while collaborating effectively across technical and business teams.

Success in this role requires strong software engineering skills, analytical thinking, problem-solving abilities, attention to detail, and the ability to design practical AI solutions that balance performance, usability, scalability, and security.

Qualifications

• Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, Data Science, or a related technical field.
• Strong understanding of large language models, natural language processing, and generative AI concepts.
• Proficiency in Python and experience developing production-quality software applications.
• Familiarity with LLM frameworks and orchestration tools such as LangChain, LlamaIndex, DSPy, or similar technologies.
• Knowledge of prompt engineering, model evaluation, and AI application development best practices.
• Experience building retrieval-augmented generation (RAG) systems and working with vector databases.
• Familiarity with embedding models, semantic search, document processing, and knowledge retrieval techniques.
• Understanding of REST APIs, microservices, and cloud-based application architectures.
• Experience with version control systems such as Git and collaborative software development workflows.
• Knowledge of machine learning fundamentals, transformer architectures, and model inference optimization.
• Familiarity with AI evaluation metrics, testing methodologies, and monitoring tools.
• Understanding of responsible AI principles, data privacy, security, and model governance considerations.
• Strong analytical, troubleshooting, and problem-solving skills.
• Excellent written and verbal communication skills with the ability to explain technical concepts to diverse audiences.
• Ability to collaborate effectively with cross-functional teams in a fast-paced development environment.
• Commitment to continuous learning and staying current with advancements in generative AI, machine learning, and software engineering.