Generative AI Engineer

Wipro; Bracknell; On-site; GBP 90,000 - 130,000; Full time

Job summary

Wipro in Bracknell, United Kingdom seeks an AI Engineer with deep Google Cloud Platform expertise to design, build and deploy production-grade ML and generative AI solutions using Vertex AI, Kubeflow, and Dataflow.

You will take models from experimental prototypes to scalable, high-availability enterprise services, collaborating with data teams to optimize pipelines, feature stores, and training datasets for robust AI workflows while managing costs and performance.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Electrical Engineering, or related field.
• 3+ years of professional experience in software or ML engineering.
• Experience deploying ML/AI models in a cloud-native production environment (GCP preferred).

Responsibilities

• Design, build, and maintain end-to-end ML and LLM pipelines using Vertex AI, Kubeflow, and Dataflow.
• Fine-tune, evaluate, and integrate foundation models via Vertex AI Model Garden into workflows using RAG architecture.
• Automate CI/CD/CT for ML models using GCP and Terraform.
• Collaborate with data teams to optimize feature stores and data pipelines for scalable AI workflows.
• Monitor model drift, latency, and throughput while optimizing resource utilization.

Skills

GCP AI ecosystem

Frameworks & Languages

Generative AI Stack

MLOps Tools

Education

Bachelor’s or Master’s degree (CS/DS/EE)

Tools

Docker

Kubernetes

Terraform

MLflow

Kubeflow Pipelines

Job description

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.