Description:
We are seeking an experienced LLM & MLOps Engineer to join our team. The ideal candidate has a strong background in developing and deploying production-grade large language models (LLMs) and Retrieval-Augmented Generation (RAG) solutions. This role involves working on cutting-edge AI technologies, building robust ML pipelines, and ensuring scalable and efficient deployments on cloud platforms.
Key Responsibilities:
LLM and RAG Solutions:
- Develop, fine-tune, and deploy LLMs for production use cases.
- Build RAG pipelines incorporating vector databases for real-time information retrieval.
- Implement and optimize solutions using frameworks like LangChain.
Chatbot Development:
- Build and enhance chatbots using LLMs and RAG frameworks.
- Ensure scalability, robustness, and performance benchmarks are met.
MLOps Pipelines:
- Design and implement end-to-end MLOps pipelines for production deployment and monitoring.
- Integrate CI/CD pipelines, model versioning, and automated workflows.
Cloud Deployment:
- Deploy and manage AI/ML solutions on Google Cloud Platform (GCP).
- Utilize GCP tools for efficient resource management and scalability.
- Knowledge of Azure and AWS is a plus.
Collaboration and Mentorship:
- Collaborate with data scientists, engineers, and product managers.
- Mentor junior team members in ML engineering and MLOps best practices.
Key Qualifications:
Experience:
- Minimum of 4 years in ML engineering with a focus on LLMs and MLOps.
Technical Expertise:
- Proficiency in developing and fine-tuning LLMs (e.g., GPT models).
- Hands-on experience with LangChain and vector databases (e.g., Pinecone, Weaviate, Milvus).
- Strong understanding of RAG architecture and implementation.
- Solid programming skills in Python with libraries like PyTorch, TensorFlow, or JAX.
MLOps Skills:
- Expertise in setting up MLOps pipelines using tools like MLflow, Kubeflow, or Vertex AI.
- Experience with CI/CD tools and version control systems (e.g., Git).
Cloud Platforms:
- Hands-on experience with GCP; knowledge of Azure and AWS is a bonus.
- Familiarity with Kubernetes, Docker, and serverless architectures.