Description

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

This role is on-site.

Responsibilities

• Design and build production-grade Gen AI applications and APIs using GCP, focusing on 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 (GPTQ, AWQ), and prompt engineering to reduce latency and improve accuracy.
• Partner with Data Science and Product teams to translate business requirements into technical AI/ML roadmaps and ensure compliance with security standards.

Required Skills

• 5+ years of industry experience in Machine Learning, with 3+ years of hands-on experience building and deploying Gen AI models and LLMs in production.
• Strong expertise in Python and standard data science libraries (scikit-learn, TensorFlow, PyTorch).
• Proven experience with Google Cloud Platform and its AI suite, including Vertex AI, BigQuery, Dataflow, and Cloud Run.
• Hands-on experience with framework tooling such as LangChain, LlamaIndex, or Hugging Face.
• Strong understanding of SQL and unstructured data management.
• Familiarity with Docker, Kubernetes (GKE), and CI/CD tools.

Preferred Skills

• Experience with multi-agent systems and orchestration (e.g., LangGraph, AutoGen).
• Deep knowledge of Vector Databases (e.g., Vertex AI Vector Search, Pinecone, Chroma).

Key Skills

Education

Any Graduate