The Senior Applied AI Engineer will design and build infrastructure for data products, focusing on data pipelines, ML solutions, and collaborating with delivery operations.
Please note: this role requires working in US time zones.
About Us
Ascent has recently been acquired by Acuity Analytics. This is both a significant milestone for us and a tremendous opportunity for you. Acuity Analytics is a business with a strong global reputation, an impressive client base and ambitious growth plans. We deliver deep insights and domain-led digital transformation to high-growth and heavily regulated organisations. To our customers, we bring a partnership that provides the talent, technology and capability to enhance performance and operational efficiency.
About the role
You’ll work at the intersection of data science, data engineering, AI engineering, and operations, embedded closely with our DaaS Delivery Operations team and cross-functional stakeholders. You’ll design and build the technical foundations that power our data products—developing data pipelines, quality systems, evaluation frameworks, and ML-assisted solutions that directly improve delivery outcomes and operational efficiency.
This role is highly execution-focused and ideal for someone who enjoys building end-to-end systems, solving complex data and ML problems in production environments, and working closely with delivery teams to unblock work through strong technical implementation. You should be comfortable owning solutions from design through deployment and iteration, with minimal reliance on hand-offs.
Skills and Experience required
What you will doData Systems & Delivery Engineering
Why join usPeople are at the Heart of our Business. By investing in people, we achieve exceptional results for our clients and create new opportunities for our teams to thrive. Check out this page for more details.
About Us
Ascent has recently been acquired by Acuity Analytics. This is both a significant milestone for us and a tremendous opportunity for you. Acuity Analytics is a business with a strong global reputation, an impressive client base and ambitious growth plans. We deliver deep insights and domain-led digital transformation to high-growth and heavily regulated organisations. To our customers, we bring a partnership that provides the talent, technology and capability to enhance performance and operational efficiency.
About the role
You’ll work at the intersection of data science, data engineering, AI engineering, and operations, embedded closely with our DaaS Delivery Operations team and cross-functional stakeholders. You’ll design and build the technical foundations that power our data products—developing data pipelines, quality systems, evaluation frameworks, and ML-assisted solutions that directly improve delivery outcomes and operational efficiency.
This role is highly execution-focused and ideal for someone who enjoys building end-to-end systems, solving complex data and ML problems in production environments, and working closely with delivery teams to unblock work through strong technical implementation. You should be comfortable owning solutions from design through deployment and iteration, with minimal reliance on hand-offs.
Skills and Experience required
- 5+ years in data science, data engineering, or ML engineering roles
- Strong proficiency in Python and SQL
- Hands-on experience with data tooling (pandas, Plotly, Streamlit, Dash)
- Practical experience working with LLMs and deploying ML solutions in production environments
- Experience integrating and working with APIs and technical systems
- Strong problem-solving skills with a bias toward implementation and delivery
- Excellent collaboration and communication skills in cross-functional teams
What you will do
- Build and maintain scalable data pipelines and transformation workflows
- Implement data quality checks, validation frameworks, and monitoring systems
- Design and operationalize evaluation frameworks for datasets and ML outputs
- Package and deliver production-ready datasets with clear documentation and QA standards
- Develop ML-assisted tools and workflows to improve data processing and delivery efficiency
- Generate, augment, and validate synthetic datasets to support client and internal use cases
- Deploy lightweight ML/LLM-powered solutions to solve operational bottlenecks
- Improve automation and repeatability across data workflows
- Work directly with delivery teams to implement and maintain production workflows
- Debug, troubleshoot, and resolve technical issues across data pipelines and systems
- Continuously improve tooling, processes, and measurement approaches used in delivery
- Identify and implement practical improvements that increase speed, reliability, and quality of delivery outcomes
Why join us
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