Machine Learning Engineer

About Indicium AI 

Indicium AI is trusted by the world's leading enterprises to deliver AI into production at scale. We are a global AI-native consultancy with proven experience across Financial Services, Energy & Utilities, Healthcare & Life Sciences, Retail & CPG, and Manufacturing. From strategy, to build, to business outcomes, we unlock value from AI with unmatched clarity, speed, and capability.

Powered by 600+ AI experts serving 50+ enterprise clients from 5 global locations, we work side-by-side with top partners - including Anthropic, Databricks, AWS, OpenAI, and Microsoft - to deliver modern AI with speed and measurable impact.

Why Indicium AI

• Fast-growing start-up organisation with huge opportunity for career growth
• Highly competitive salary package along with company bonus
• A hugely collaborative working environment where every person’s viewpoint is considered - a chance to make your mark on the business from day one!
• Financially backed business meaning security and support for new initiatives and global market expansion
• Pick your own Gear! Macbooks, PCs, Accessories!

Overview

We are looking for a Machine Learning Engineer with 3+ years of experience to design, build, and operationalise scalable ML solutions in AWS. This role is focused on end-to-end ML engineering and MLOps, with strong hands-on experience in Amazon SageMaker and production-grade ML systems.

Key Responsibilities

• Design, develop, and deploy machine learning models in AWS (primarily SageMaker).
• Build and maintain scalable ML pipelines for data preparation, training, evaluation, and deployment.
• Productionise ML workflows, including CI/CD for ML, model versioning, and experiment tracking.
• Implement monitoring systems for model performance, data quality, and model/data drift.
• Optimise model performance, scalability, and cost-efficiency in cloud environments.
• Collaborate with data scientists, data engineers, and platform teams to deliver robust ML solutions.

Required Qualifications

• 3+ years of experience in ML Engineering or MLOps roles.
• Strong hands-on experience with AWS, especially SageMaker (training jobs, endpoints, pipelines).
• Strong Python skills and experience with common ML libraries (e.g., scikit-learn, XGBoost, PyTorch, TensorFlow).
• Experience deploying and maintaining ML models in production environments.
• Experience with monitoring, logging, and drift detection frameworks.
• Familiarity with CI/CD, containerisation (Docker), and infrastructure-as-code practices.

Nice to Have

• Experience with ML feature stores and experiment tracking tools.
• Knowledge of distributed training and large-scale data processing.
• Experience working in data-driven product or consulting environments.

Benefits

• Holiday Entitlement - 25 days holiday plus bank holidays
• Learning & Development: 1.500€ training budget + 5 training days
• Company Bonus - Discretionary company and personal bonus paid quarterly
• Pension Scheme
• Choose Your Kit - Select from a range of laptops and accessories
• Social Events: meeting - Ups, Squad events, Summer/ Christmas events, etc.
• And many others.