MLOps Engineer

Anticipated Contract End Date/Length: December 31 2026
Work set up: Hybrid 23 days on site per week (must be eligible for BPSS)

Our client in the Information Technology and Services industry is looking for a MLOps Engineer with strong expertise in AWS-native services infrastructure automation and end-to-end ML workflow operations. The role focuses on designing scalable ML infrastructure operationalising machine learning solutions and implementing best practices across the ML lifecycle using CloudFormation SageMaker Glue Lambda Spark and Python. This position plays a key role in ensuring secure scalable and compliant ML workloads aligned with enterprise standards while enabling reliable model deployment and monitoring in production environments.

What you will do:

• Design automate and maintain scalable ML infrastructure using AWS CloudFormation and AWS-native services.
• Build deploy and manage ML models on Amazon SageMaker including training tuning hosting endpoints and pipelines.
• Develop and optimise 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 lifecycle including 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 addressing data drift concept drift and performance degradation.
• Ensure secure scalable and compliant ML workloads aligned with enterprise standards including IAM KMS networking and observability.
• Partner with data scientists data engineers and cloud architects to operationalise ML solutions in production.
• Troubleshoot ML pipelines model deployment issues and infrastructure bottlenecks.

Qualifications :

• 47 years of hands-on experience in MLOps ML engineering or cloud-based automation roles.
• Strong expertise in AWS CloudFormation for infrastructure as code automation.
• Solid experience with Amazon SageMaker including training inference pipelines and model registry.
• Strong hands-on experience with AWS Glue and Apache Spark for ETL and distributed data processing.
• Proficiency in Python for ML and ETL automation and production pipelines.
• Strong understanding of ML lifecycle management data preprocessing and feature engineering model evaluation versioning deployment strategies and performance monitoring and alerting.
• Experience with CI/CD pipelines for ML using tools such as CodePipeline GitHub Actions Jenkins or similar.
• Good understanding of ML frameworks such as TensorFlow PyTorch or Scikit-learn for integration and packaging.
• Strong knowledge of AWS services relevant to ML and automation including Lambda S3 Step Functions IAM KMS and CloudWatch.

Additional Information :

All your information will be kept confidential according to EEO guidelines.

Candidates must be legally authorized to live and work in the country where the position is based without requiring employer sponsorship.

HelloKindred is committed to fair transparent and inclusive hiring practices. We assess candidates based on skills experience and role-related requirements.

We appreciate your interest in this opportunity. While we review every application carefully only candidates selected for an interview will be contacted.

HelloKindred is an equal opportunity employer. We welcome applicants of all backgrounds and do not discriminate on the basis of race colour religion sex gender identity or expression sexual orientation age national origin disability veteran status or any other protected characteristic under applicable law.

Remote Work :

No

Employment Type :

Contract