Senior Data Engineer (AI & MLOps) – Software – Newcastle/Hybrid or Remote
Day rate: £300 – £500 (Inside IR35)
Duration: 6 months
Start: ASAP
My new client is looking for a Senior Data Engineer with expertise in AI, MLOps, and AWS architecture to design and deliver production-grade machine learning pipelines. The ideal candidate will be passionate about bridging the gap between data science experimentation and scalable production systems, driving automation, and enabling faster innovation cycles.
Key Responsibilities
Architect, build, and maintain production-grade ML Ops pipelines to automate deployment, monitoring, and scaling of machine learning models.
Collaborate with data scientists and ML engineers to reduce time-to-production for experiments and prototypes.
Design and optimize data wrangling and transformation workflows using Python.
Leverage AWS cloud services (EC2, S3, Lambda, SageMaker, RDS, DynamoDB, Redshift, etc.) to build robust, scalable, and cost-effective solutions.
Apply AIOps practices to enhance monitoring, automation, and resilience of ML systems.
Implement best practices in data engineering, version control, CI/CD, and infrastructure as code.
Ensure the security, reliability, and compliance of data pipelines and deployed ML solutions.
Mentor junior engineers and contribute to setting technical standards for the team.
Required Qualifications
Proven experience as a Senior Data Engineer, MLOps Engineer, or similar role.
Strong background in data structures, algorithms, and software engineering principles.
Advanced proficiency in Python for data wrangling, pipeline automation, and ML workflows.
Expertise in AWS services, including databases (RDS, DynamoDB, Redshift) and machine learning/AI (SageMaker, AI/ML frameworks).
Hands-on experience with ML pipeline orchestration, CI/CD, and deployment automation.
Deep understanding of ML Ops practices, including monitoring, scaling, and retraining strategies.
Familiarity with AIOps concepts and tools for operational automation.
Preferred Skills
Experience with data science and machine learning model development.
Knowledge of containerization (Docker, Kubernetes, EKS).
Exposure to infrastructure-as-code (Terraform, CloudFormation).
Strong problem-solving, communication, and collaboration skills.
*Rates depend on experience and client requirements
