MLOps Engineer

Job Description

Anticipated Contract End Date/Length: December 31, 2026

Work set up: Hybrid, 2–3 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

• 4–7 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.