Jobs at Ascent.io

You will develop data pipelines, integrate APIs, prepare data for GenAI processing, and ensure code quality and scalability.

As a Senior Data Scientist, you will enhance AI solutions by developing evaluation datasets and pipelines, tuning retrieval performance, and collaborating with teams to improve model quality and user experience.

Lead end-to-end fullstack and Generative AI solutions: architect, build, deploy, and operate production systems. Design LLM/RAG capabilities, own DevOps (CI/CD, IaC, containers, orchestration), drive technical proposals with customers, enforce engineering standards, and mentor engineers while supporting pre-sales and discovery.

Design, build, and operate cloud-native fullstack applications and platforms on Microsoft Azure. Own feature development, containerized deployments, CI/CD, infrastructure-as-code, observability, and governance. Collaborate with cross-functional teams to deliver scalable, reliable solutions.

Lead cloud and platform engineering strategy, define architecture and standards, design and review complex Azure cloud-native platforms, champion platform engineering (IDPs, CI/CD, Kubernetes, IaC), resolve critical technical challenges, drive SRE/observability and developer productivity improvements, mentor engineering leaders, and represent the organisation externally.

Design and develop cloud-native applications and integration services in Azure, using Kubernetes and Python while implementing CI/CD practices and troubleshooting production issues.

The role involves designing, building, and maintaining Azure-based integration solutions, utilizing Kubernetes and workflow orchestration tools while collaborating with teams in a DevOps environment.

The role involves designing and developing scalable data pipelines using Azure Databricks, building transformations with PySpark and SQL, and working with graph databases to support analytics and data modelling in a regulated environment.

Coordinate and own program-level testing across integration, UAT, and production environments. Create and maintain test plans, run smoke/integration/E2E tests, manage test reporting and quality gates, facilitate status and Go/No-Go meetings, and guide testers through all test phases while supporting release coordination.

As a Data Scientist in Pre-Sales, you will implement data-driven solutions, support sales with analytics expertise, and collaborate with technical teams on AI and machine learning projects.

Design and optimize ML/AI pipelines using Azure Databricks, implement CI/CD pipelines, and ensure compliance in AI solutions.

The role involves supporting pharmaceutical clients by delivering analytics solutions, analyzing clinical datasets, and collaborating with diverse stakeholders.

The Senior BI & Data Engineer will build scalable Power BI dashboards and work with Azure Databricks to develop curated data layers, ensuring data quality and optimizing performance for reporting.

Lead end-to-end technical recruitment for software, data, analytics and cloud roles in Bulgaria. Design sourcing strategies, build diverse pipelines, manage hiring processes, advise hiring managers on market trends and compensation, deliver strong candidate experience, report on hiring metrics, improve TA processes, and mentor junior team members.

Lead technical initiatives within Kallidus, building and optimizing scalable solutions, mentoring junior engineers, and collaborating across teams.

Lead and manage the DACH business application landscape, ensuring alignment with IT strategy and overseeing key systems while ensuring compliance and driving system improvements.

Drive growth of AI, Data, and Digital services in the DACH region, manage enterprise sales cycles, and build client relationships in financial services.

The Senior Data & AI Solution Architect will design scalable solutions, engage with clients, oversee implementations, and innovate in the Data & AI field.

Lead the design and maintenance of Azure cloud infrastructure, implement CI/CD pipelines, and ensure operational best practices. Collaborate with teams on cloud governance and continuous improvement of DevOps processes.

As a Data Scientist, implement data-driven solutions and collaborate in developing AI, machine learning models, and proof of concepts for diverse clients.