Top Tech Jobs & Startup Jobs

Build compliant, high-quality real-time data pipelines for CDR, network telemetry, and customer data. Enable Kafka and Flink streaming for AI use cases, implement data quality, lineage, and governance controls, manage feature engineering pipelines for machine learning models, and ensure compliance with data privacy regulations. Success is measured through real-time readiness, data quality, and pipeline reliability.

Lead Salesforce-to-Dynamics 365 CRM migrations by assessing environments, defining migration strategies, mapping and transforming data, configuring Dynamics 365, migrating integrations, supporting testing and cutover, and providing training and post-go-live support. The role requires strong Salesforce, Dynamics 365, Dataverse, Power Platform, data migration, integration, stakeholder management, and Agile delivery expertise.

Own the AI platform roadmap, backlog, use-case prioritization, stakeholder alignment, adoption, usability, cost transparency, and ROI tracking across telecommunications domains. The role focuses on delivering business value through revenue growth, churn reduction, automation, and network optimization while aligning platform delivery with business-unit needs and strategic priorities.

Own and configure the SAP SuccessFactors Employee Central module, including data standards, documentation, enhancements, support, issue resolution, release management, data quality, and reporting. Translate HR requirements into system solutions, improve and automate processes, troubleshoot integrations and workflows, collaborate with SAP Support, and advise HR, IT, leadership, and global stakeholders on HR transformation initiatives.

Ensure the reliability, performance, availability, and cost efficiency of AI services in production telco environments. Define SLIs and SLOs, monitor model drift and AI performance, manage incident response and root cause analysis, support disaster recovery and high availability, and optimize GPU usage and inference scaling for mission-critical fraud and network operations use cases.

Owns enterprise AI architecture across telecom network, customer, and revenue domains. Defines scalable GenAI, ML, and agentic system architectures; integrates AI with OSS/BSS platforms and network data sources; establishes RAG, real-time inference, and AI-agent reference architectures; and develops governance, TRiSM, model-risk, security, compliance, and multi-country deployment standards across cloud, edge, latency, and data-sovereignty requirements.

Product Engineer responsible for taking digital product features from discovery through production and support. The role involves translating business needs into scalable technical solutions, developing secure and maintainable software, collaborating with multidisciplinary teams, and contributing to engineering practices and AI-assisted development. The engineer will work with stakeholders, product owners, testers, designers, and fellow engineers while supporting continuous improvement and operational stability.

Identify and pursue UK sales opportunities, manage the full sales cycle from prospecting through closing, exceed revenue targets, build lasting client and partner relationships, conduct targeted outreach and networking, negotiate effectively, overcome objections, and adapt sales strategies based on industry trends.

Lead the EMEA Big Data Analytics and Data Insights team, managing data engineers and scientists, setting objectives, allocating resources, and conducting reviews. Transform complex data into enterprise data-layer insights, support IT projects, contribute technically, and help shape global data warehouse, data lake, and Lakehouse architecture. The role requires strong leadership, data architecture expertise, business collaboration, and approximately 10% travel.

Build and operate a shared AI platform for telecom squads. Responsibilities include developing model-serving, vector database, feature store, and prompt orchestration services; enabling self-service APIs, SDKs, and pipelines; integrating Kafka, streaming, and data lakes; managing GPU workloads and inference scaling; and supporting low-latency applications such as real-time fraud detection.