Description

Design and deliver production-grade Generative AI solutions on Google Cloud Platform, focusing on scalable systems using Gemini models, RAG architectures, and agentic workflows.

This role is on-site.

Responsibilities

• Design, build, and deploy production-ready Generative AI applications and APIs on GCP.
• Implement Retrieval-Augmented Generation (RAG) pipelines using vector databases and unstructured data sources.
• Develop end-to-end MLOps pipelines for training, evaluation, deployment, and monitoring using Vertex AI, Kubeflow, Cloud Build, and Terraform.
• Optimize model performance through fine-tuning, prompt engineering, and compression techniques like GPTQ and AWQ.
• Architect scalable, cost-efficient GCP infrastructure with strong emphasis on security, IAM, and VPC design.

Required Skills

• 8+ years of industry experience in Machine Learning.
• 3+ years of hands-on experience building and deploying Generative AI / LLM-based solutions in production.
• Strong experience with Google Cloud Platform, including Vertex AI, BigQuery, Dataflow, and Cloud Run.
• Advanced proficiency in Python and ML frameworks such as TensorFlow, PyTorch, and scikit-learn.
• Hands-on experience with LLM frameworks like LangChain, LlamaIndex, or Hugging Face.
• Solid understanding of SQL and unstructured data processing.
• Experience with Docker, Kubernetes (GKE), and CI/CD pipelines.
• Ability to collaborate cross-functionally and provide technical leadership.

Preferred Skills

• Experience with multi-agent systems and orchestration tools such as LangGraph or AutoGen.
• Deep understanding of vector databases including Vertex AI Vector Search, Pinecone, or Chroma.
• Google Cloud Professional Machine Learning Engineer certification.

Key Skills

Education

Any Graduate