Jobs at Lingaro

Designs and oversees scalable data engineering architectures and implementations. The role translates business requirements into data solutions, guides engineering teams, ensures data quality and governance, optimizes performance, integrates systems, documents architecture, and supports project planning. It requires extensive Azure and Databricks expertise, proficiency in Python, PySpark, and SQL, knowledge of distributed computing and modern data platforms, and experience leading teams and working with stakeholders.

Lead a global Microsoft Power Platform and Copilot adoption pilot from discovery through read-out. Manage senior client stakeholders, translate technical requirements into learning journeys, oversee change and communications, coordinate learning designers and a delivery squad, and manage fixed-price delivery, risks, budget, and scope. Measure adoption and value, conduct train-the-trainer handover, and package the pilot into a reusable market rollout blueprint.

Designs enterprise data architecture, governance frameworks, data models, platforms, databases, ETL pipelines, and data quality processes. Collaborates with stakeholders to align data strategy with business objectives, documents data journeys, evaluates technologies, optimizes performance and security, and guides development teams. The role also supports data integration, warehousing, lakehouse, analytics, monitoring, training, and adoption of architecture best practices.

Develops instructional design materials, including learning objectives, course outlines, scripts, storyboards, and e-learning courses. Builds content in Articulate Rise and Storyline, applies adult learning principles, collaborates with subject matter experts and account teams, presents work to clients, incorporates feedback, and manages multiple projects. Uses AI-enabled tools and ensures accessibility, LMS, SCORM, xAPI, and quality standards are addressed.

Build and productionize machine learning, generative AI, LLM, and automation solutions. Design industrialized processing pipelines, implement MLOps and LLMOps best practices, develop enterprise AI agents using Microsoft Copilot Studio and Azure AI Foundry, and integrate Microsoft and Azure services. Gather requirements, estimate work, present technical solutions, troubleshoot agents, and create documentation.

Develop test plans, test cases, and automated testing frameworks using Playwright and TypeScript. Execute functional, regression, API, and performance testing; identify, prioritize, and track defects; collaborate with developers and stakeholders; document testing processes; and improve QA methodologies and automation coverage.

Design, build, and optimize GCP data ecosystems, including pipelines, data lakes, warehouses, storage architectures, and processing infrastructure. Gather requirements, model data, troubleshoot complex issues, tune performance, monitor systems, and support new functionality. Collaborate directly with clients and cross-functional teams, while mentoring junior data engineers and leading technical improvement discussions.

Lead data modeling for SAP domains including Supply Chain, Order-to-Cash, and Finance. Design and review conceptual, logical, and physical models; validate mappings, data structures, business rules, and data contracts; and ensure governance, quality, metadata, lineage, and canonical model alignment. Use Databricks and SQL to support integration, transformation, reporting, and analytics. Collaborate with data, engineering, SAP, architecture, and business stakeholders to resolve modeling gaps and approve production-ready designs.

Provides technical leadership for Azure data engineering projects, including solution architecture, data modeling, pipelines, data integration, code quality, QA, performance optimization, governance, security, and documentation. Collaborates with stakeholders and project managers to define scope, manage risks, and deliver scalable, efficient solutions. Requires strong Python, SQL, PySpark, Databricks, Azure Data Factory, Delta Lake, Synapse, Spark, data warehousing, governance, and Azure DevOps expertise.

Lead GenAI solution design and build production-grade applications, including RAG pipelines, LLM integrations, agents, fine-tuning, evaluation, guardrails, and enterprise APIs. Collaborate across technical teams, estimate work, mentor engineers, develop reusable components, and validate emerging AI frameworks. The role requires strong Python, machine learning, cloud, CI/CD, containerization, and production LLM development experience.

Build and productionize machine learning, LLM, and AI solutions with Data Science teams. Design industrialized data-processing pipelines, implement MLOps and LLMOps frameworks, manage model lifecycles, and develop production Python, ML, and cloud solutions using Azure, GCP, Databricks, Spark, and PySpark. Gather requirements, estimate work, document solutions, present results, and support technical best practices.

Build and evolve reusable AI capabilities for construction software products. Design production-grade LLM, retrieval, tool-use, orchestration, and agentic systems; establish evaluation-driven development and reliability standards; and prototype scalable AI solutions. Collaborate with product, engineering, and business stakeholders, guide other engineers, improve AI-assisted development practices, and support systems from experimentation through production rollout and continuous improvement.

The Data Scientist will implement GenAI systems, build components, support project delivery, analyze findings, and deliver technical solutions, focusing on NLP and deep learning.

Analyze FMCG sales, pricing, promotions, customer, product, margin, market share, and competitor data to support revenue growth management. Develop pricing and price elasticity models, identify commercial opportunities, create visualizations and actionable insights, and translate analytical needs into product requirements, user stories, and business rules. Collaborate with stakeholders, product managers, data engineers, developers, and commercial teams to build scalable digital analytics solutions.

Implement enterprise ML and GenAI solutions in production, including AI agents, LLM automation, RAG, knowledge grounding, and industrialized processing pipelines. Establish ML lifecycle, MLOps, and LLMOps best practices; integrate Microsoft AI and Power Platform services; support deployments across environments; gather requirements; estimate work; present solutions; and document technical implementations.

Implement machine learning models in production and design enterprise GenAI, LLM, and AI automation solutions. Build and manage industrialized processing pipelines, establish ML lifecycle and MLOps/LLMOps best practices, integrate Microsoft AI and Power Platform services, and support data science teams. The role also involves gathering requirements, estimating work, presenting technical solutions, troubleshooting AI agents, and creating documentation.

Develop end-to-end classification and forecasting models, including data preparation, exploratory analysis, feature engineering, model training, tuning, evaluation, visualization, and deployment support. Collaborate with data and AI engineers on productionization, monitoring, dashboards, and reproducible documentation. Apply Python, SQL, classical machine learning, time-series modeling, cloud platforms, and business-focused analytics to solve problems such as demand forecasting and churn prediction.

Implement machine learning models in production and design enterprise GenAI solutions, AI agents, and scalable LLM/ML automation. Build and manage industrialized processing pipelines, MLOps and LLMOps frameworks, and model lifecycle best practices. Integrate Microsoft Copilot Studio and Azure AI services with Power Platform tools, implement RAG and knowledge grounding, support multilingual retrieval, and troubleshoot agents. Gather requirements, estimate work, present technical solutions, and create documentation.

Owns end-to-end delivery of technical IT projects across data, analytics, BI, web development, and data platform domains. Translates business needs into scope, priorities, and solutions while managing budgets, timelines, risks, dependencies, and technical constraints. Partners with clients and technical teams, leads Agile or hybrid ceremonies, supports backlog prioritization and acceptance criteria, resolves blockers, and ensures effective documentation and knowledge transfer.

Develop and expand supply chain analytics offerings focused on planning, forecasting, inventory optimization, demand planning, and procurement. Advise clients, support pre-sales and solution design, guide delivery teams, and translate supply chain needs into data, analytics, and AI-enabled solutions. Mentor consultants, build reusable assets, collaborate with technology partners, and contribute to thought leadership and capability development.