LLM Engineer

Design, implement, and optimize NLP models, including traditional ML-based NLP, LMs, and LLMs.

Fine-tune and evaluate large-scale pre-trained models for domain-specific tasks.

Develop and maintain robust text data generation and processing pipelines.

Collaborate with product and engineering teams to build NLP-powered applications from research to deployment.

Conduct experiments and benchmark models using quantitative evaluation techniques.

Stay up to date with the latest advancements in LLMs (e.g., RAG, MCP, tool use, agentic systems) and apply them effectively in projects.

Integrate NLP components into larger production systems, ensuring scalability and performance.

Follow best practices in software engineering, version control, testing, and cloud-based deployment.

2+ years of professional experience in LLM, Natural Language Processing.

Strong proficiency in Python, especially for data processing and model development.

Solid understanding of NLP concepts such as tokenization, embedding, language modeling, transformers, and sequence modeling.

Experience with LLM fine-tuning, prompt engineering, pretraining, and downstream evaluation.

Familiarity with traditional NLP techniques and modern LLM-based approaches.

Hands-on experience in building data pipelines for training and evaluation.

Experience working on end-to-end NLP product development (research to deployment).

Proficient with Git and cloud platforms like GCP (or AWS, Azure).

Exposure to DevOps practices, model deployment, and monitoring in production environments.

Deep knowledge or hands-on experience with agentic LLM systems, tool calling, retrieval-augmented generation (RAG), and Model Context Protocol (MCP) workflows.

Experience with open-source LLM frameworks such as Hugging Face Transformers, LangChain, LlamaIndex, or similar.

Exposure to LLM-based application development platforms or orchestration tools.

Understanding of software architecture, microservices, and scalable systems.

Opportunity to work on cutting-edge LLM/NLP problems with real product impact

Collaborative and research-friendly engineering culture

Flexible work environment

Career growth aligned with emerging AI innovations