LLM Engineer (Large Language Model Engineer)Role Description

We are seeking an innovative and technically skilled LLM Engineer to design, develop, deploy, and optimize applications powered by Large Language Models (LLMs) and Generative AI technologies. The successful candidate will be responsible for building intelligent AI solutions that leverage foundation models to automate workflows, enhance user experiences, and solve complex business problems. This role involves close collaboration with machine learning engineers, software developers, data scientists, product managers, and business stakeholders to deliver scalable, secure, and production-ready AI systems.

Key responsibilities include designing and developing LLM-powered applications, AI assistants, chatbots, retrieval-augmented generation (RAG) systems, AI agents, and intelligent automation solutions; integrating foundation models through APIs and open-source frameworks to support a wide range of business use cases; implementing prompt engineering strategies, prompt templates, function calling, structured output generation, and workflow orchestration; developing and optimizing Retrieval-Augmented Generation (RAG) pipelines using vector databases, embeddings, semantic search, and knowledge retrieval techniques; fine-tuning, evaluating, and optimizing open-source or proprietary language models where appropriate; building scalable AI services, RESTful APIs, and backend components for production deployment; collaborating with data engineering teams to prepare datasets, document processing pipelines, and knowledge bases for AI applications; implementing model monitoring, evaluation frameworks, observability, and performance optimization to improve response quality, latency, and reliability; ensuring AI applications meet security, privacy, compliance, and responsible AI standards; conducting experimentation with emerging LLM architectures, multimodal AI, agentic workflows, and reasoning techniques; participating in architecture reviews, technical design discussions, and code reviews; documenting AI system designs, deployment processes, and best practices; and contributing to continuous improvement of AI engineering standards, development workflows, and platform capabilities.

The LLM Engineer is expected to combine expertise in artificial intelligence, machine learning, software engineering, and cloud technologies to build scalable and reliable Generative AI solutions. Success in this role requires strong problem-solving skills, technical curiosity, and the ability to translate cutting-edge AI capabilities into practical business applications.

Qualifications

• Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, Information Technology, or a related discipline.
• Strong understanding of Large Language Models (LLMs), Generative AI, Natural Language Processing (NLP), and transformer-based architectures.
• Proficiency in Python and experience developing production-quality software.
• Experience with AI frameworks and libraries such as PyTorch, TensorFlow, Hugging Face Transformers, LangChain, LlamaIndex, Haystack, DSPy, or similar technologies.
• Knowledge of prompt engineering, Retrieval-Augmented Generation (RAG), embeddings, semantic search, vector databases, and AI agent architectures.
• Familiarity with vector databases such as Pinecone, Weaviate, Milvus, Chroma, FAISS, Qdrant, or similar platforms.
• Experience integrating AI services and model APIs from providers such as OpenAI, Anthropic, Google, Azure AI, AWS Bedrock, or open-source foundation models.
• Understanding of RESTful APIs, microservices, distributed systems, and software architecture principles.
• Familiarity with cloud platforms including AWS, Microsoft Azure, Google Cloud Platform (GCP), or similar environments.
• Knowledge of containerization and orchestration technologies such as Docker and Kubernetes.
• Understanding of software development best practices, including Git, CI/CD pipelines, testing, code reviews, and Agile methodologies.
• Experience with SQL, NoSQL databases, data processing, and data engineering concepts.
• Strong analytical, debugging, and problem-solving skills.
• Excellent written and verbal communication skills with the ability to explain technical concepts to both technical and non-technical stakeholders.
• Knowledge of AI evaluation methodologies, model observability, responsible AI, AI security, privacy, and governance principles is highly desirable.
• Professional certifications in cloud computing, machine learning, artificial intelligence, or software engineering are considered advantageous but are not mandatory.
• Demonstrated commitment to continuous learning and staying current with the rapidly evolving Generative AI ecosystem, emerging foundation models, agent frameworks, and industry best practices.