Jobs at Lingaro

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.

Business Analyst responsible for requirements management, process analysis, solution design, data governance, KPI definition, process modeling, and UAT leadership. The role partners with business, data, analytics, architecture, and delivery teams to implement scalable solutions, improve operational efficiency, ensure data quality, and support reporting and digital transformation initiatives. Finance and sales domain expertise, SAP knowledge, cloud data platform experience, and familiarity with dbt and data lineage tools are preferred.

Designs and maintains automated UI, API, integration, and data tests using Playwright, TypeScript or JavaScript, and related frameworks. Builds scalable automation frameworks, validates databases, ETL processes, data pipelines, and data quality, and works with Azure environments. Defines testing strategies, identifies quality risks, improves reliability and coverage, integrates tests into CI/CD pipelines, and uses AI tools to improve test creation, analysis, and debugging.

Business Analyst gathers and governs requirements, maps business processes and data flows, defines KPIs and data-quality rules, maintains data catalogs and metadata, and advises stakeholders on solutions. The role translates requirements into functional specifications, user stories, and acceptance criteria; leads UAT planning, defect triage, and business sign-off; and coordinates testing, deployment, and implementation. Experience with SAP, cloud data platforms, dbt, data lineage, APIs, and AI-assisted delivery tools is advantageous.

Designs and implements enterprise data architectures, governance frameworks, data models, integration pipelines, data platforms, and quality processes. The role uses Azure Synapse, Azure Data Factory, and Azure Data Lake, while optimizing databases, security, performance, and recovery. Responsibilities include stakeholder requirements gathering, data journey documentation, technology evaluation, data profiling and cleansing, technical guidance, and training. Requires strong communication, leadership, and at least six years of data and analytics experience.

Develop and maintain scalable AWS data engineering platforms and reusable Python modules. Build object-oriented data processing solutions, debug pipelines across Step Functions, Glue, Athena, and Lambda, and apply modern engineering practices including testing, CI/CD, and version control. Collaborate with agile teams to deliver reliable solutions across countries and business environments.

Lead and mentor a data engineering team while providing technical direction, troubleshooting support, and architectural guidance. Oversee scalable data solutions, data modeling, pipelines, quality assurance, coding standards, and performance optimization. Collaborate with stakeholders and project managers to deliver projects on schedule, manage risks, maintain documentation, and ensure security and regulatory compliance. Serve as a customer advisor and primary technical point of contact.

Designs scalable data architectures, models, pipelines, ETL frameworks, governance strategies, and cloud-based data solutions. Collaborates with stakeholders, data scientists, analysts, and engineering teams to translate business needs into technical implementations. Provides technical leadership and mentorship, oversees implementation, ensures data quality and security, optimizes performance, documents architecture, and supports project planning.

Design and implement Azure-based data processing systems, scalable data pipelines, data warehouses, data lakes, and ETL workflows. Transform and optimize structured and unstructured data, tune queries, improve storage and retrieval performance, and resolve system bottlenecks. Collaborate with data scientists, analysts, and stakeholders to deliver technical solutions while working within Agile methodologies.

Designs and oversees scalable GCP data architectures and infrastructure. Defines data models, schemas, pipelines, governance, security, and quality practices; selects and integrates data technologies; guides engineering teams; supports implementation, optimization, documentation, and project planning. Collaborates with stakeholders, analysts, and data scientists to translate business needs into reliable data engineering solutions.