Description

You will design and deploy scalable, secure, and reliable LLM-based solutions.

Responsibilities

• Design, build, and maintain production-grade Generative AI applications and APIs on GCP, using Gemini models and RAG architectures.
• Develop automated MLOps pipelines for training, evaluation, monitoring, and deployment using Vertex AI, Kubeflow, and Cloud Build.
• Implement model enhancement techniques, including fine-tuning, quantization, and prompt engineering to improve accuracy and reduce latency.
• Optimize GCP resources for high-performance computing while ensuring security (IAM, VPC) and cost-efficiency.
• Partner with cross-functional teams to translate business needs into technical AI/ML roadmaps.

Required Skills

• 5-8+ years in Machine Learning, with 3+ years deploying Generative AI models in production.
• Proven experience with Google Cloud Platform (GCP), including Vertex AI, BigQuery, Dataflow, and Cloud Run.
• Strong expertise in Python and data science libraries like Scikit-learn, TensorFlow, and PyTorch.
• Hands-on experience with LLM frameworks such as LangChain.
• Strong understanding of SQL and unstructured data management.
• Familiarity with Docker, Kubernetes (GKE), and CI/CD tooling.
• Experience designing and implementing Retrieval-Augmented Generation (RAG) systems.

Key Skills

Education

Any Graduate