Senior Data Engineer (AI & MLOps, AWS, Python) – Software
Posted Today by 1758191867
£500 Per day
Inside
Hybrid
Tyne and Wear
Summary: The Senior Data Engineer role focuses on designing and delivering production-grade machine learning pipelines, leveraging expertise in AI, MLOps, and AWS architecture. The position requires collaboration with data scientists and ML engineers to enhance automation and innovation cycles. The ideal candidate will also mentor junior engineers and implement best practices in data engineering. This role is available on a hybrid or remote basis in Newcastle.
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.
Key Skills:
- 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.
Salary (Rate): £500 daily
City: Newcastle
Country: United Kingdom
Working Arrangements: hybrid
IR35 Status: inside IR35
Seniority Level: Senior
Industry: IT
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