Description

Lead the design and deployment of scalable, secure, and reliable LLM-based solutions on Google Cloud Platform. You will own the end-to-end lifecycle of Generative AI applications, from architecture to production monitoring.

This role is on-site.

Responsibilities

• Design and build production-grade Generative AI applications and APIs using Gemini models, RAG architectures, and vector databases.
• Develop automated MLOps pipelines for training, evaluation, monitoring, and deployment using Vertex AI, Kubeflow, Cloud Build, and Terraform.
• Implement model optimization techniques including fine-tuning, quantization, and prompt engineering to enhance accuracy and reduce latency.
• Optimize GCP resources for high-performance computing, ensuring scalability, cost-efficiency, and security compliance.
• Partner with Data Science and Product teams to translate business requirements into technical AI/ML roadmaps.

Required Skills

• 8+ years of industry experience in Machine Learning, with at least 3+ years of hands-on experience building and deploying Generative AI models and LLMs in production.
• Strong expertise in Google Cloud Platform (GCP) and its AI suite, including Vertex AI, BigQuery, Dataflow, and Cloud Run.
• Advanced proficiency in Python and standard data science libraries such as scikit-learn, TensorFlow, and PyTorch.
• Hands-on experience with GenAI framework tooling including LangChain, LlamaIndex, or Hugging Face.
• Deep understanding of SQL and unstructured data management.
• Practical experience with Docker, Kubernetes (GKE), and CI/CD tools.
• Ability to implement Retrieval-Augmented Generation (RAG) and agentic workflows to solve complex business problems.

Preferred Skills

• Experience with multi-agent systems and orchestration frameworks like LangGraph or AutoGen.
• Deep knowledge of Vector Databases such as Vertex AI Vector Search, Pinecone, or Chroma.

Key Skills

Education

Any Graduate