Senior AI Engineer: MLOps & Cloud AI in Hybrid Role

SmartRecruiters, Inc.; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Benefits offered by this job

Hybrid work

Private medical insurance

Pension plan

Life assurance

Certifications supported

Employee discounts

ERGs

Job summary

Netcompany is seeking a Senior AI Engineer to build, deploy, and maintain ML and AI solutions for clients across public and private sectors. You’ll work with data scientists and engineers through data prep, model development, production deployment, and monitoring.

You’ll contribute to data science solution design, apply ML techniques, and help maintain scalable MLOps pipelines while collaborating with clients and internal teams. Mentorship and governance are part of the role.

Qualifications

• Proficiency in Python and C# (SQL or R is a plus).
• Experience with cloud AI services (Azure ML, AWS SageMaker, Vertex AI).
• Hands-on with ML libraries (scikit-learn, TensorFlow, PyTorch).
• Knowledge of DL/ML algorithms and evaluation methods.
• Experience building observability and monitoring for AI systems.
• Experience handling large, messy datasets.
• Understanding of public sector data and regulations is a plus.

Responsibilities

• Design, develop, deploy, and maintain AI/ML solutions across the full lifecycle.
• Collaborate with data scientists, engineers, and consultants on solutions.
• Build production ML models and contribute to MLOps pipelines.
• Translate requirements into technical components and deliverables.
• Communicate findings clearly to both technical and non-technical audiences.
• Mentor junior team members and share knowledge.

Skills

Python

C#

SQL

R

Cloud AI services

Azure ML

AWS SageMaker

GCP Vertex AI

scikit-learn

TensorFlow

PyTorch

ML algorithms

model evaluation

MLOps

data handling

Job description

Netcompany is seeking a Senior AI Engineer to build, deploy, and maintain ML and AI solutions for clients across public and private sectors. You’ll work with data scientists and engineers through data prep, model development, production deployment, and monitoring.

You’ll contribute to data science solution design, apply ML techniques, and help maintain scalable MLOps pipelines while collaborating with clients and internal teams. Mentorship and governance are part of the role.