Description

You will design, build, and deploy production-ready Generative AI applications and APIs on GCP, while providing technical leadership to the team.

This role is on-site.

Responsibilities

• Implement Retrieval-Augmented Generation (RAG) pipelines using vector databases and unstructured data sources.
• Develop and manage 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 such as GPTQ and AWQ.
• Architect scalable, cost-efficient GCP infrastructure with a strong focus on security, IAM, and VPC design.
• Collaborate cross-functionally to define AI/ML technical roadmaps and ensure adherence to data privacy and ethical AI standards.

Required Skills

• 5–8+ years of industry experience in Machine Learning.
• 3+ years of hands-on experience building and deploying Generative AI or LLM-based solutions in production.
• Strong proficiency in Google Cloud Platform (GCP), 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 including LangChain, LlamaIndex, or Hugging Face.
• Solid understanding of SQL and unstructured data processing.
• Experience with Docker, Kubernetes (GKE), and CI/CD pipelines.

Preferred Skills

• Experience with multi-agent systems and orchestration (LangGraph, AutoGen, etc.).
• Deep understanding of vector databases (Vertex AI Vector Search, Pinecone, Chroma).
• Google Cloud Professional Machine Learning Engineer certification.

Key Skills

Education

Bachelor's degree