What are the responsibilities and job description for the AI Engineers position at Blueberry AI?
**About Blueberry AI**
- We offer an AI content management system that specializes in professional design file management, particularly for 3D assets that can be previewed on Chrome without being downloaded locally (Maya, 3d Max, etc.). Our system allows for direct online previews to reduce software costs and helps build a knowledge-based library to increase asset reuse.
Our system also leverages the content stored by our enterprise clients to train their private AI models, similar to an enterprise-specific ChatGPT. This system is deployed on the clients' own servers. For instance, clients like Haier can request detailed organizational information, such as industrial design details, ad ROI performance, or even competitors' information.
This enables them to receive quick, company-specific answers that large language models cannot provide.
**Qualifications**
To succeed in this role, you will need:
- Proven Experience: At least 3 years of experience in AI/ML engineering, with a focus on NLP, LLMs, and retrieval systems.
- Technical Proficiency: Strong programming skills in Python, with experience in frameworks like TensorFlow, PyTorch, Hugging Face, or LangChain.
- LLM Expertise: Hands-on experience with large language models, including fine-tuning and prompt engineering.
- RAG Knowledge: Familiarity with retrieval-augmented generation methods and tools, such as vector databases (e.g., Pinecone, Weaviate, FAISS) and search technologies (e.g., Elasticsearch, OpenSearch).
- Data Pipelines: Experience building and optimizing data pipelines for large-scale applications.
- Cloud Computing: Proficiency with cloud platforms (AWS, GCP, Azure) for model training and deployment.
**Key Responsibilities**
You will be responsible for:
- Developing and Optimizing LLMs: Fine-tune, optimize, and deploy large language models (GPT, LLaMA, etc.) to meet specific application needs.
- Designing RAG Pipelines: Build retrieval-augmented generation architectures, combining LLMs with vector databases, search engines, and custom retrievers.
- Data Engineering: Curate, preprocess, and manage large-scale datasets for model training and evaluation, ensuring high-quality data for fine-tuning and RAG workflows.
- Model Evaluation: Design and execute testing frameworks to assess performance, scalability, and accuracy of LLMs and RAG systems.
- Integration and Deployment: Collaborate with software engineers to integrate AI solutions into production systems, ensuring seamless deployment and real-time performance.
- Scalability and Optimization: Develop strategies to optimize computational efficiency, reduce latency, and enhance system reliability.
- Collaborative Problem Solving: Work closely with cross-functional teams, including product managers, data scientists, and software engineers, to align AI capabilities with business goals.
- Staying Current: Monitor and experiment with emerging research in NLP, RAG, and related fields to keep solutions cutting-edge.
**Preferred Skills:**
- Knowledge of distributed systems, parallel processing, and MLOps frameworks.
- Understanding of security best practices for handling sensitive data and models in production.
- Contributions to open-source projects in the LLM or RAG domains.
- Strong communication skills and the ability to explain complex concepts to non-technical stakeholders.
**Why Choose Us?**
We offer:
- An opportunity to work on cutting-edge AI technologies that redefine industry standards.
- A collaborative and innovative team culture.
- A competitive salary and benefits package.
- A flexible work environment with a focus on work-life balance.
**How to Apply:**
Please submit your resume, a cover letter, and links to any relevant projects or repositories.
