Sql developer állások VII. kerületben (ai és automatizáció)

• Design, build, and deploy production-grade agentic AI and automation solutions that collect, validate, and quality-assure data at scale
• Engineer multi-agent orchestrations, data pipelines, MCP integrations, and API connections that power autonomous, end-to-end data workflows

• Ownership of data engineering features, architecture, and code quality
• Design, implementation, and maintenance of Databricks-based data pipelines and workflows
• Building and optimization of ETL/ELT processes using Apache Spark on Databricks

• Technical management of a team of 2-3 AI and cloud engineers focused on fast prototyping of AI/ML solutions in AWS cloud
• Mentoring and technical support to junior and medium teammates
• Acting as a trusted advisor to clients

• Design and own secure, scalable services
• Guide technical decisions and evaluate trade-offs including AI-driven components
• Leverage AI tools to accelerate coding, debugging, testing, and prototyping

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• Assessment and clarification of customer functional requirements related to the system/software jointly with business consultants
• Development and modification of applications or part of the application
• Preparation of related documentation

• Co-design and implement QA workflows using statistical diagnostics, validation rules, and anomaly detection techniques
• Application of AI/machine learning tools to identify inconsistencies, missing values, or suspicious patterns in large-scale asset datasets

• Design and build scalable, reusable frameworks and services for data ingestion, transformation, and processing
• Develop and maintain the platform’s core capabilities such as orchestration, data quality validation, and schema management tools

• Design new features in a flagship SaaS product
• Code new features in a flagship SaaS product
• Review new features in a flagship SaaS product
• Test new features in a flagship SaaS product
• Refactor legacy software components
• Participate in large-scale design of the future state of the product

• Definition and improvement of metrics representing product performance and operations efficiency
• Data collection from multiple sources ensuring accuracy and completeness
• Data cleaning, organization, and transformation into usable formats for analysis

• Design, build, and maintain backend services and APIs using C#/.NET Core in a cloud (AWS) environment
• Contribute to the development of scalable and secure microservices supporting SaaS products
• Work with Angular to implement and support end-to-end features where needed

• Build-from-zero applications powered by the HawkAI Engine
• Bridge machine learning models and real-time data pipelines into APIs and user interfaces
• Integrate AI-assisted development tools into software architecture and data integrity workflows

• Building and maintaining SQL queries and reports that track bookings, pipeline coverage, and conversion
• Monitoring CRM data quality to resolve issues at the source
• Analyzing pipeline trends and supporting the weekly forecasting review cadence with clean, accurate data

• Resource account igénylése és összekötése namespacekkel
• Resource accountba kerülő komponensek megvalósítása RDS, APIk, S3, MSK, cognito, SES, private link endpoint és endpoint service nonprod és prod környezeteken
• RICE API kapcsolatok kialakítása

• Definition and improvement of metrics reflecting product performance, service quality, and operational efficiency
• Analysis of client behavior and service processes to identify trends, bottlenecks, inefficiencies, and drivers of performance

• Lead the architecture, design, and implementation of enterprise-scale web applications using modern frontend technologies
• Serve as the technical lead for UI development while remaining actively involved in coding, design reviews, and implementation

• Design, build, review, test, and maintain Ruby on Rails features and services in line with Diligent’s agile engineering practices
• Own technical delivery for projects, epics, and major user stories, including solution design, implementation planning, and execution through to production

• Design and own secure, scalable services
• Guide technical decisions and evaluate trade-offs including AI-driven components
• Architect and deliver AI/agentic product features including LLM integrations, multi-step agentic workflows, tool-use patterns, RAG pipelines, and intelligent automation

• Design and own secure, scalable services
• Guide technical decisions and evaluate trade-offs including AI-driven components
• Leverage AI tools to accelerate coding, debugging, testing, and prototyping
• Rigorously validate AI outputs to ensure quality and safety

• Design and implement data fabric architecture across heterogeneous data sources and platforms
• Develop scalable ETL/ELT pipelines for batch and real-time data integration
• Integrate structured and unstructured data from databases, APIs, SaaS applications, data lakes, and enterprise systems