LLM Engineer

Job Summary:

We are seeking a highly motivated and skilled LLM Engineer with 2 to 7 years of professional experience to join our growing AI team. The ideal candidate will have a strong background in natural language processing, machine learning, and hands-on experience in developing, deploying, and optimizing solutions built upon Large Language Models. You will play a crucial role in designing, implementing, and maintaining robust and scalable LLM-powered applications, contributing to the full lifecycle of our AI products.

Key Responsibilities:

• LLM Application Development:

  Design, develop, and deploy innovative applications leveraging state-of-the-art Large Language Models (e.g., GPT, Llama, Gemini, Claude, etc.). This includes working with various LLM APIs and open-source models.

• Prompt Engineering & Optimization:

  Develop and refine advanced prompt engineering techniques to maximize LLM performance, accuracy, and desired output for specific use cases.

• Fine-tuning & Adaptation:

  Experiment with and implement strategies for fine-tuning pre-trained LLMs on custom datasets to improve performance for domain-specific tasks.

• Data Preparation & Curation:

  Work with diverse datasets for training, fine-tuning, and evaluating LLMs, ensuring data quality, relevance, and ethical considerations.

• Model Evaluation & Benchmarking:

  Design and execute robust evaluation methodologies to assess LLM performance, identify biases, and ensure alignment with business objectives. Implement A/B testing and other experimentation frameworks.

• Integration & Deployment:

  Integrate LLM-powered solutions into existing systems and deploy them to production environments, ensuring scalability, reliability, and low latency. Experience with MLOps practices is highly desirable.

• Performance Optimization:

  Identify and implement strategies for optimizing LLM inference, resource utilization, and cost efficiency.

• Research & Innovation:

  Stay abreast of the latest advancements in LLMs, NLP, and machine learning research. Proactively explore and propose new technologies and approaches to enhance our AI capabilities.

• Collaboration:

  Work closely with cross-functional teams including data scientists, software engineers, product managers, and researchers to deliver impactful AI solutions.

• Documentation:

  Create clear and concise documentation for models, code, and deployment procedures.

Required Qualifications:

• Bachelor&aposs or Master&aposs degree in Computer Science, Artificial Intelligence, Machine Learning, Computational Linguistics, or a related quantitative field.

• 2-7 years of professional experience

  in a role focused on Machine Learning, Natural Language Processing, or AI development.

• Strong proficiency in Python

  and relevant ML/DL frameworks (e.g., TensorFlow, PyTorch, Hugging Face Transformers).

• Hands-on experience with Large Language Models (LLMs)

  , including familiarity with their architectures (e.g., Transformers) and practical application.
• Experience with prompt engineering techniques and strategies.
• Solid understanding of NLP concepts, including text pre-processing, embeddings, semantic search, and information retrieval.
• Familiarity with cloud platforms (AWS, GCP, Azure) and their AI/ML services.
• Experience with version control systems (e.g., Git).
• Excellent problem-solving skills and the ability to work independently and as part of a team.
• Strong communication and interpersonal skills to articulate complex technical concepts to both technical and non-technical audiences.

Preferred Qualifications (Bonus Points for):

• Experience with fine-tuning LLMs on custom datasets.
• Familiarity with MLOps tools and practices (e.g., MLflow, Kubeflow, Docker, Kubernetes).
• Experience with vector databases (e.g., Pinecone, Weaviate, Milvus) for RAG applications.
• Knowledge of various retrieval techniques for Retrieval Augmented Generation (RAG) systems.
• Understanding of ethical AI principles, bias detection, and fairness in LLMs.
• Contributions to open-source projects or relevant publications.
• Experience with distributed computing frameworks.