MLOps Engineer (CloudFormation, SageMaker, Glue, Lambda, Spark, Python) - Glasgow and remote

MLOps Engineer (CloudFormation, SageMaker, Glue, Lambda, Spark, Python) - Glasgow and remote - 11 months+/RATE: £306 per day inside IR35

One of our Blue Chip Clients is urgently looking for an MLOps Engineer (CloudFormation, SageMaker, Glue, Lambda, Spark, Python

For this role you will need to be onsite in Glasgow 2-3 days per week.

Please find some details below:

CONTRACTOR MUST BE ELIGIBLE FOR BPSS

MUST BE PAYE THROUGH UMBRELLA

Role Overview
We are seeking a highly skilled MLOps Engineer with strong expertise in AWS-native services, infrastructure automation, and end to end ML workflow operations. The ideal candidate will have deep experience with CloudFormation, SageMaker, Glue, Lambda, Spark, and Python, along with solid understanding of machine learning concepts, data pipelines, model monitoring, and production deployment.
 
Key Responsibilities
Design, automate, and maintain scalable ML infrastructure using AWS CloudFormation and AWS-native services.
Build, deploy, and manage ML models on Amazon SageMaker (training, tuning, hosting, endpoints, pipelines).
Develop and optimize distributed data processing workflows using Apache Spark, AWS Glue, and related ETL frameworks.
Build serverless automation and integration logic using AWS Lambda and Python-based microservices.
Implement MLOps best practices across the ML life cycle-data preprocessing, feature engineering, model training, testing, deployment, and monitoring.
Create reproducible and automated model CI/CD pipelines integrating data, code, and infrastructure components.
Establish continuous model monitoring frameworks (data drift, concept drift, performance degradation).
Ensure secure, scalable, and compliant ML workloads aligned with enterprise standards (IAM, KMS, networking, observability).
Partner with data scientists, data engineers, and cloud architects to operationalize ML solutions in production.
Troubleshoot ML pipelines, model deployment issues, and infrastructure bottlenecks.
 
Required Skills & Experience
4-7 years of hands on experience in MLOps, ML engineering, or cloud-based automation roles.
Strong expertise in AWS CloudFormation for IaC automation (must have).
Solid experience with Amazon SageMaker (training, inference, pipelines, model registry).
Strong hands on experience with AWS Glue and Apache Spark for ETL and distributed data processing.
Proficiency in Python, particularly for ML/ETL automation and production pipelines.

Strong understanding of:
o ML life cycle management
o Data preprocessing & feature engineering
o Model evaluation, versioning, and deployment strategies
o Model performance monitoring & alerting
Experience with CI/CD pipelines for ML (CodePipeline, GitHub Actions, Jenkins, or similar).
Good understanding of ML frameworks (TensorFlow, PyTorch, Scikit-learn) for integration and packaging.
Strong knowledge of AWS services relevant to ML and automation (Lambda, S3, Step Functions, IAM, KMS, CloudWatch).

Please send CV for full details and immediate interviews. We are a preferred supplier to the client.