Generative AI Engineer

About the Company

We are looking for a skilled AI Engineer with deep expertise in Google Cloud Platform (GCP) to design, build, and deploy production-grade machine learning and generative AI solutions. You will be responsible for taking AI models from experimental prototypes to scalable, high-availability enterprise services using GCP’s native AI/ML ecosystem.

About the Role

We are looking for a skilled AI Engineer with deep expertise in Google Cloud Platform (GCP) to design, build, and deploy production-grade machine learning and generative AI solutions.

Responsibilities

• Model Deployment \& Pipeline Architecture:

Design, build, and maintain end-to-end ML and LLM pipelines using Vertex AI, Kubeflow, and Dataflow.

• Generative AI \& LLM Integration:

Fine-tune, evaluate, and integrate foundation models (e.g., Gemini) via Vertex AI Model Garden into application workflows using RAG architecture.

• MLOps \& Infrastructure:

Automate continuous integration, deployment, and monitoring (CI/CD/CT) for machine learning models using GCP infrastructure and Terraform.

• Data Engineering Collaboration:

Work with data teams to optimize feature stores, data pipelines (BigQuery, Pub/Sub), and training datasets for scalable AI workflows.

• Performance \& Cost Optimization:

Monitor model drift, latency, and throughput in production while optimizing GCP resource utilization and infrastructure costs.

Qualifications

• Technical Skills
• GCP AI Ecosystem:

Hands-on experience with Vertex AI (Pipelines, Feature Store, Model Registry, Endpoint deployment), BigQuery ML, and Cloud Run/GKE.

• Frameworks \& Languages:

Proficiency in Python and standard ML frameworks (PyTorch, TensorFlow, JAX).

• Generative AI Stack:

Experience with orchestration frameworks (LangChain, LlamaIndex), vector databases (Vertex AI Vector Search, Pinecone, or pgvector), and prompt engineering/tuning.

• MLOps Tools:

Familiarity with Docker, Kubernetes, Terraform, MLflow, or Kubeflow Pipelines.

Experience \& Background

• 3+ years of professional experience in software engineering, machine learning engineering, or data science.
• Proven track record of deploying and maintaining ML/AI models in a cloud-native production environment (preferably GCP).
• Bachelor’s or Master’s degree in Computer Science, Data Science, Electrical Engineering, or a related quantitative field (or equivalent practical experience).

Preferred Skills

• Google Cloud Certified - Professional Machine Learning Engineer or Professional Cloud Architect.
• Experience with serverless AI applications and event-driven architectures on GCP.