GenAI & MLOps AI Engineer — Scale & Secure AI Platforms

Wipro Ltd.; Greater London; On-site; GBP 85,000 - 120,000; Full time

Job summary

Wipro Ltd. in London is seeking an AI Engineer to join the engineering team. You will work closely with software, infrastructure, and operations to build scalable AI-driven platforms and services for internal users.

The role emphasizes GenAI, LLMs, and advanced analytics, with responsibilities across data engineering, model deployment, monitoring, and collaboration with cross-functional teams to translate business problems into practical AI solutions.

Qualifications

• Proven experience in MLOps, data engineering, or software development with a recent GenAI focus.
• Understanding of version control, CI/CD, containerisation, and monitoring in ML/MLOps.
• Strong Python or R coding and ML library experience; practical model deployment skills.
• Experience with large-scale data, SQL, Spark, and cloud platforms (AWS/GCP).
• Ability to translate business problems into AI/ML solutions and communicate results.

Responsibilities

• Collaborate with data engineers, developers, and UI/UX teams to deliver AI-powered platforms.
• Build scalable, secure, and observable ML/AI solutions for internal users.
• Prototype, deploy, monitor, and improve GenAI models and workflows.

Skills

Python

R

scikit-learn

TensorFlow

PyTorch

FastAPI

LangChain

LLM APIs

embeddings

vector databases

RAG workflows

agentic AI systems

Spark

AWS

GCP

on-prem hybrid

data visualization

communication

cross-functional collaboration

version control

Education

BS in Computer Science

Engineering

Statistics

Data Science

equivalent work experience

Tools

Spark

Redis

RabbitMQ

LangChain

LlamaIndex

Job description

Wipro Ltd. in London is seeking an AI Engineer to join the engineering team. You will work closely with software, infrastructure, and operations to build scalable AI-driven platforms and services for internal users.

The role emphasizes GenAI, LLMs, and advanced analytics, with responsibilities across data engineering, model deployment, monitoring, and collaboration with cross-functional teams to translate business problems into practical AI solutions.