Top Tech Jobs & Startup Jobs

Owns the development, validation, and continuous improvement of AI-powered analytics solutions, including Talk2Data and agent-ready data. Defines analytical requirements, evaluation metrics, quality frameworks, and data models; validates large datasets and AI outputs; collaborates with data engineers and business stakeholders; supports governance, deployment, enablement, and proof-of-concept initiatives; and evaluates AI tools and integrations.

Own financial controlling for service lines, including management reporting, budgeting, forecasting, variance analysis, profitability, utilization, margin, cash flow, and working capital reporting. Build Excel and BI dashboards, support month-end close, track KPIs, improve reporting processes, and help implement financial systems. The role partners with Accounting, regional and Group leadership, and business teams while delivering actionable analyses and process improvements.

Develop and maintain high-availability frontend solutions using React, TypeScript, JavaScript, and microservices. Design new projects, improve customer experience and platform architecture, apply data-driven experimentation and A/B testing, and troubleshoot performance and browser compatibility issues. Mentor developers, strengthen coding and testing practices, and collaborate with product teams, stakeholders, and external partners. The role also involves using AI-powered development assistants and contributing to CI/CD and cloud-based delivery.

Design and develop an AI-powered analytics agent in Devin, integrating GitHub, dbt, Databricks, Snowflake, and data ingestion systems. Adapt dbt workloads for Databricks, resolve platform compatibility issues, build and test agent capabilities, and implement guardrails, validation, observability, security, and reliable AI-assisted workflows. Collaborate with client engineering teams, troubleshoot integrations, document the solution, and prepare knowledge-transfer materials.

Validate migrated and reporting data across source and target systems, with emphasis on SQL-based reconciliation, data quality checks, ETL/ELT validation, discrepancy analysis, and root-cause investigation. Define test scenarios, validate dashboards and KPIs, document defects, and collaborate with Data Engineers and stakeholders. The role also involves testing structured and semi-structured data, profiling datasets, using Python for automation, and supporting modern cloud data-platform delivery.

Designs and implements automation for incidents, service requests, and operational workflows. Builds Infrastructure-as-Code and deployment automation, improves monitoring and alerting, contributes to self-healing and auto-remediation, and applies software engineering practices. Collaborates with Platform Operations, SRE, and Service Management teams while developing reusable automation frameworks and mentoring engineers. Requires cloud, Kubernetes, CI/CD, observability, Python, and AI-assisted development experience.

Design and build responsive native iOS applications using SwiftUI and UIKit. Implement secure OAuth/OIDC authentication, token exchanges, networking, and integrations with external web services. Optimize performance, stability, memory usage, and quality while developing unit and end-to-end tests. Participate in code reviews, TDD, App Store releases, and modern AI-assisted software delivery.

Serve as the senior technical authority for Salesforce AMS, leading architecture, governance, L3 escalations, root-cause analysis, technical-debt remediation, code reviews, integrations, and scalable solution design. Provide technical leadership across Apex, Flow, LWC/Aura, APIs, and Salesforce security. The role requires Salesforce architect-level expertise, AI assistant experience, strong governance and support skills, Italian and English proficiency, and availability to work from the Milan office two days weekly.

Lead the end-to-end design, development, deployment, and maintenance of production AI and machine learning solutions. Define architecture standards, technical strategy, MLOps practices, governance, responsible AI controls, and AI Act compliance. Build and monitor models, advise stakeholders, troubleshoot production systems, and mentor engineers. The role also supports Generative AI, LLM applications, document processing, RAG, and scalable cloud-based ML platforms.

Designs, builds, evaluates, deploys, monitors, and improves machine learning and generative AI solutions. Responsibilities include data preparation, feature engineering, model lifecycle ownership, production troubleshooting, CI/CD, observability, model governance, responsible AI, and collaboration with engineers, architects, and business stakeholders.