Mid/Senior LLM Developer @ DEV12

2 weeks ago

Remote, Czech Republic DEV12 Full time
Required Qualifications
  • Python Expertise: Minimum 3 years of professional experience in Python development.
  • Machine Learning Background: At least 3 years of hands-on experience in building machine learning solutions (preferably NLP).
  • LLM Proficiency:
    • At least 1 year of commercial or academic experience working with Large Language Models.
    • Familiarity with local LLM frameworks and third-party model APIs (e.g. GPT-4, Claude, or Bard).
  • API Development: Experience in creating and integrating RESTful and GraphQL APIs.
  • Cloud Expertise: Hands-on experience with GCP, AWS, or Azure cloud platforms for AI deployments.


Preferred Qualifications
  • Deep LLM Knowledge:
    • Experience with fine-tuning models using Hugging Face, PyTorch, or TensorFlow.
    • Expertise in prompt optimization and hyperparameter tuning for task-specific applications.
  • Vector Database Integration: Practical knowledge of tools like Pinecone, Weaviate, FAISS, or Milvus for efficient RAG pipeline design.
  • Tooling and Deployment: Familiarity with Docker, Kubernetes, and CI/CD pipelines for deploying scalable AI solutions.
  • Cloud-Native Services:
    • Proficiency with Vertex AI (GCP), SageMaker (AWS), or Azure ML for training and deploying LLMs.
    • Understanding of cost optimization and autoscaling in cloud environments.
  • MLOps Practices: Experience with tools like DVC, MLflow, and Kubeflow for managing model lifecycles and deployments.
  • Quantization and Optimization:
    • Familiarity with techniques for model quantization (e.g. INT8, FP16) to improve efficiency and reduce resource consumption.
    • Hands-on experience with frameworks like ONNX Runtime, TensorRT, or DeepSpeed for deploying optimized and quantized models.

We are looking for an experienced Mid/Senior LLM Developer to join our team and contribute to innovative AI-driven projects. You will play a pivotal role in designing, building, and deploying solutions leveraging Large Language Models (LLMs) to solve complex business challenges. This position involves working with a modern LLM stack, focusing on scalability, performance, and seamless integration with cloud platforms.

In this role, you will enjoy a high level of autonomy, both in choosing the stack and shaping the final outcomes of your projects. We value independent thinkers who can take ownership of their work and drive innovation. Additionally, this position offers the opportunity to grow into a team leadership role, guiding and mentoring a group of talented developers while shaping the future of our AI solutions.

While many projects will involve collaboration within a team, some shorter, high-priority projects may require you to take full ownership as a single developer, from design to deployment.

,[Design and implement solutions using Large Language Models (LLMs) for various use cases such as natural language understanding, question-answering, and conversational AI., Build and optimize Retrieval-Augmented Generation (RAG) pipelines by integrating LLMs with vector databases., Utilize both local models (e.g. Llama) and third-party APIs (e.g. OpenAI, Anthropic) to develop robust AI solutions., Work with a comprehensive LLM stack, including tools for model training, fine-tuning, and deployment:, Frameworks: Hugging Face Transformers, LangChain, PyTorch, TensorFlow, Vector Databases: Pinecone, Weaviate, FAISS, Milvus, RAG Integration Tools: LangChain, LlamaIndex, Develop and maintain APIs using REST and GraphQL to enable smooth integration with external systems., Employ prompt engineering techniques to fine-tune LLMs for specific tasks, improving accuracy and relevance., Implement MLOps best practices for versioning, monitoring, and managing the lifecycle of models in production., Collaborate with teams to deploy models on cloud platforms like AWS (SageMaker), GCP (Vertex AI), or Azure (ML Studio), ensuring scalability and cost-efficiency., Keep up-to-date with advancements in LLM research, emerging frameworks, and AI trends] Requirements: Python, Machine Learning, NLP, API, REST API, GraphQL, Cloud, GCP, AWS, Azure, Cloud platform, PyTorch, TensorFlow, Docker, Kubernetes, CD pipelines, AI, Azure ML, Autoscaling, MLOps, MLflow, Kubeflow Additionally: International projects, Flat structure, Small teams.

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