DataObrii

Cloud Engineer/DevOps

Part-time

DataObrii, a dynamic Data Science company based in the UK, is seeking a talented Cloud Engineer/DevOps with strong knowledge of AWS to join our team on a part-time remote basis. As a Cloud Engineer/DevOps, you will play a crucial role in developing and maintaining our computational systems for machine learning and big data analysis. We are looking for candidates with expertise in cloud technologies, particularly AWS and Azure, as well as proficiency in DevOps practices and tools.

Key Responsibilities:

- Design, deploy, maintain, and troubleshoot AWS cloud infrastructure, including EC2, Lambda, S3, SQS, DynamoDB, IAM, VPC, CloudWatch, API Gateway, and related services.
- Build and maintain cloud infrastructure using Terraform and Infrastructure as Code (IaC) practices.
- Develop and maintain CI/CD pipelines using GitLab CI, Jenkins, or similar tools.
- Deploy, maintain, and troubleshoot Docker-based applications and development environments.
- Develop Python and Bash scripts to automate infrastructure, operational, and data-processing tasks.
- Administer Linux-based systems, troubleshoot system and application issues, and manage access, networking, permissions, services, and system configurations.
- Work with relational and non-relational databases, including database connectivity, access management, troubleshooting, and basic administration.
- Support and maintain data-processing and ETL workloads running in AWS.
- Monitor cloud infrastructure and applications, investigate incidents, and improve system reliability and performance.
- Manage AWS security and access controls using IAM, security groups, networking, secrets management, and least-privilege practices.
- Monitor and optimise AWS resource usage and infrastructure costs.
- Maintain technical documentation for infrastructure, configurations, access procedures, and operational processes.

Required Skills and Qualifications:

- At least 2 years of hands-on experience in a Cloud Engineer, DevOps Engineer, or similar role, with a strong focus on AWS.

- Strong Python development skills beyond scripting, with experience designing, developing, testing, and maintaining production-grade Python applications and services.

- Strong practical knowledge of AWS services, including EC2, Lambda, IAM, VPC, S3, SQS, DynamoDB, CloudWatch, and API Gateway.

- Hands-on experience with Terraform and Infrastructure as Code (IaC) practices.

- Experience building and maintaining CI/CD pipelines using GitLab CI, Jenkins, or similar tools.

- Practical experience with Docker and containerised environments.

- Strong Linux administration and troubleshooting skills.

- Good understanding of networking concepts, including TCP/IP, DNS, routing, firewalls, security groups, and private/public networking.

- Experience working with relational and non-relational databases.

- Good understanding of database connectivity, permissions, basic administration, and troubleshooting.

- Good understanding of cloud security, IAM, access management, and secrets management.

Nice to Have:

- Experience with Kubernetes and AWS EKS.

- Experience with AWS Glue and ETL/data-processing workflows.

- Experience with Amazon Redshift and data warehouse environments.

- Experience with dbt or dbt Cloud.

- Experience with Microsoft Azure.

- Experience administering Windows Server environments.

- AWS or other relevant cloud certifications.

General:

Bachelor's degree in Computer Science, Information Technology or a related field.

Strong troubleshooting and problem-solving skills.

Good command of spoken and written English.

We are looking for a Middle Data Scientist with strong hands-on experience in time-series, deep learning, and Python development.

This role is a good fit for someone with 3+ years of professional experience in Data Science or Machine Learning, a strong understanding of neural networks, and practical experience developing predictive models for time-series and tabular data.

We expect solid knowledge of different neural network architectures, including recurrent and convolutional networks, as well as practical experience designing, training, tuning, validating, and deploying models.

Strong Python programming skills are essential for this position. You should also be confident working with statistics, data analysis, and production-oriented Data Science code.

What you will work on:

• Perform exploratory data analysis and prepare statistical summaries for hypothesis testing and data-driven decision-making.
• Build data preparation and feature engineering pipelines used by predictive models.
• Design and implement predictive modelling approaches.
• Continuously analyse and improve models already running in production.
• Take an active role in Python development related to end-to-end ML model delivery and support internal ML-related libraries.
• Tune loss functions, evaluation metrics, sample weights, hyperparameters, and other model components to improve model quality.

• Prepare detailed model evaluation reports, summarise findings, and communicate practical insights to stakeholders.

   

What we expect from you:

• At least 3 years of professional experience working as a Data Scientist or Machine Learning Engineer.
• Practical experience building custom neural networks with PyTorch.
• Strong knowledge of neural network architectures applicable to time-series forecasting and predictive modelling of tabular data.
• Experience working with layers such as LSTM, GRU, Conv1D, Dense, Dropout, and BatchNormalization, as well as activation functions including ReLU, Sigmoid, Tanh, and Softmax.
• Hands-on experience working with time-series datasets, including data loading, cleaning, preparation, and feature engineering with pandas and NumPy.
• Experience with the Darts library would be a significant advantage.
• Experience selecting and developing models available in Darts, as well as creating custom neural network architectures.

• Excellent Python knowledge and confidence working with libraries such as Scikit-learn, Pandas, PyTorch, and SHAP.

   

Python proficiency is particularly important for this role. Our projects require Data Scientists who can independently write, structure, and maintain Data Science functionality and supporting libraries.

You should also have:

• An understanding of statistics and data analysis.
• Experience selecting and tuning loss functions such as MSE, MAE, Cross-Entropy Loss, as well as custom losses suitable for forecasting problems.
• Experience with hyperparameter optimisation tools such as Optuna or Hyperopt.
• Ability to implement and optimise PyTorch training loops, including data loaders and training/validation stages.
• Knowledge of cross-validation approaches designed specifically for time-series data.
• Understanding of regularisation techniques used to reduce overfitting.
• Strong knowledge of model evaluation metrics, including MSE, RMSE, MAE, MAPE, and R-squared.
• Familiarity with backtesting approaches used to validate model performance.
• Experience with model deployment, including model serialisation and deployment for real-time services.
• Experience preparing visualisations with matplotlib, seaborn, or Plotly.
• Confident use of Git for version control and team collaboration.
• Strong self-management and organisational skills.

What We Offer:

• Competitive remuneration package.
• Bonuses
• Professional mentorship and guidance from experienced team members.
• Opportunities for professional growth and continuous learning.
• A dynamic and collaborative work environment.