Design, develop, and optimize scalable ETL/ELT data pipelines using Databricks, PySpark, and SQL, and build agentic workflows to automate data engineering processes.
Compensation
Competitive compensation: USD-based pay with education, fitness, and team activity budgets.
Responsibilities
Design, develop, and optimize scalable ETL/ELT data pipelines using Databricks, PySpark, and SQL.
Build and operationalize agentic workflows to automate data engineering and operational processes such as data validation, issue identification, troubleshooting, and workflow execution.
Integrate agentic capabilities with existing Databricks, GCP, BigQuery, and Delta Lake environments.
Develop data pipelines and reusable frameworks and components to support new business requirements and datasets across multiple use cases.
Implement data quality checks, monitoring, validation, exception handling, and production controls.
Requirements
Nice to have: AI/agentic workflows
Nice to have: AI agents
Nice to have: workflow automation
Nice to have: orchestration frameworks
Nice to have: reusable data engineering frameworks
Design, develop, and optimize scalable ETL/ELT data pipelines using Databricks, PySpark, and SQL, and build and operationalize agentic workflows to automate data engineering and operational processes.
Compensation
Competitive compensation: USD-based pay with education, fitness, and team activity budgets.
Responsibilities
Design, develop, and optimize scalable ETL/ELT data pipelines using Databricks, PySpark, and SQL.
Build and operationalize agentic workflows to automate data engineering and operational processes such as data validation, issue identification, troubleshooting, and workflow execution.
Integrate agentic capabilities with existing Databricks, GCP, BigQuery, and Delta Lake environments.
Develop data pipelines and reusable frameworks and components to support new business requirements and datasets across multiple use cases.
Implement data quality checks, monitoring, validation, exception handling, and production controls.
Requirements
Nice to have: AI/agentic workflows
Nice to have: AI agents
Nice to have: workflow automation
Nice to have: orchestration frameworks
Nice to have: reusable data engineering frameworks
Design, develop, and optimize scalable ETL/ELT data pipelines using Databricks, PySpark, and SQL, and operationalize agentic workflows to automate data engineering processes.
Compensation
Competitive compensation: USD-based pay with education, fitness, and team activity budgets.
Responsibilities
Design, develop, and optimize scalable ETL/ELT data pipelines using Databricks, PySpark, and SQL.
Build and operationalize agentic workflows to automate data engineering and operational processes such as data validation, issue identification, troubleshooting, and workflow execution.
Integrate agentic capabilities with existing Databricks, GCP, BigQuery, and Delta Lake environments.
Develop data pipelines and reusable frameworks and components to support new business requirements and datasets across multiple use cases.
Implement data quality checks, monitoring, validation, exception handling, and production controls.
Requirements
Nice to have: AI/agentic workflows
Nice to have: AI agents
Nice to have: workflow automation
Nice to have: orchestration frameworks
Nice to have: reusable data engineering frameworks
Design, develop, and optimize scalable ETL/ELT data pipelines using Databricks, PySpark, and SQL, and operationalize agentic workflows for data engineering automation.
Compensation
Competitive compensation: USD-based pay with education, fitness, and team activity budgets.
Responsibilities
Design, develop, and optimize scalable ETL/ELT data pipelines using Databricks, PySpark, and SQL.
Build and operationalize agentic workflows to automate data engineering and operational processes such as data validation, issue identification, troubleshooting, and workflow execution.
Integrate agentic capabilities with existing Databricks, GCP, BigQuery, and Delta Lake environments.
Develop data pipelines and reusable frameworks and components to support new business requirements and datasets across multiple use cases.
Implement data quality checks, monitoring, validation, exception handling, and production controls.
Requirements
Nice to have: AI/agentic workflows
Nice to have: AI agents
Nice to have: workflow automation
Nice to have: orchestration frameworks
Nice to have: reusable data engineering frameworks
Design, develop, and optimize scalable ETL/ELT data pipelines using Databricks, PySpark, and SQL, and operationalize agentic workflows for data engineering automation.
Compensation
Competitive compensation in INR
Responsibilities
Design, develop, and optimize scalable ETL/ELT data pipelines using Databricks, PySpark, and SQL.
Build and operationalize agentic workflows to automate data engineering and operational processes such as data validation, issue identification, troubleshooting, and workflow execution.
Integrate agentic capabilities with existing Databricks, GCP, BigQuery, and Delta Lake environments.
Develop data pipelines and reusable frameworks and components to support new business requirements and datasets across multiple use cases.
Implement data quality checks, monitoring, validation, exception handling, and production controls.
Requirements
Nice to have: AI/agentic workflows
Nice to have: AI agents
Nice to have: workflow automation
Nice to have: orchestration frameworks
Nice to have: reusable data engineering frameworks
Design, develop, and optimize scalable ETL/ELT data pipelines using Databricks, PySpark, and SQL.
Compensation
Competitive compensation in INR
Responsibilities
Design, develop, and optimize scalable ETL/ELT data pipelines using Databricks, PySpark, and SQL.
Build and operationalize agentic workflows to automate data engineering and operational processes such as data validation, issue identification, troubleshooting, and workflow execution.
Integrate agentic capabilities with existing Databricks, GCP, BigQuery, and Delta Lake environments.
Develop data pipelines and reusable frameworks and components to support new business requirements and datasets across multiple use cases.
Implement data quality checks, monitoring, validation, exception handling, and production controls.
Requirements
Nice to have: AI/agentic workflows
Nice to have: AI agents
Nice to have: workflow automation
Nice to have: orchestration frameworks
Nice to have: reusable data engineering frameworks
Lead and contribute to research initiatives in AI-driven digital circuit design for image sensors, pioneering new methodologies and developing novel machine learning algorithms and tools.
Responsibilities
Lead and participate in research initiatives in AI-driven digital circuit design
Pioneer new methodologies for integrating AI/ML models into traditional EDA workflows
Develop novel machine learning algorithms and AI-assisted tools for digital design challenges
Requirements
PhD in electrical engineering, machine learning, computer science, applied physics or related field
Enterprise AI SME / Architect to drive long‑term AI adoption across application development and production support teams.
Responsibilities
Partner with application and production support teams to understand their technology landscape, operational challenges, and manual/repetitive activities.
Identify, prioritize, and recommend AI/GenAI use cases that improve engineering productivity, production support efficiency, reliability, and speed of delivery.
Recommend the appropriate AI platform/tool—GitHub Copilot, ChatGPT, Claude, Databricks AI, or other enterprise AI solutions—based on the use case.
Drive AI opportunities in areas such as code development, testing, documentation, incident analysis, log analysis, root‑cause analysis, knowledge management, and support automation.
Design solutions using LLMs, RAG, AI agents, agentic workflows, enterprise search, and automation.
Requirements
Nice to have: 12+ years of overall IT experience
Nice to have: strong architecture/technical leadership experience
Nice to have: recent hands‑on experience delivering enterprise GenAI/AI solutions
Nice to have: experience working across multiple application development and production support teams
Nice to have: experience taking AI solutions from use‑case identification and POC through production implementation and adoption
Lead quality and mission assurance activities for Strategic Deterrent Systems, driving continuous improvement, root cause analysis, and compliance with AS9100/ISO 9001.
Design, implement and refine models that turn multi-modal data into production solutions across a wide range of industries and business problems.
Locations (2)
Santiago
Sao Paulo
Responsibilities
Build, test, and refine machine learning and analytical models that power production systems across a range of problem types, such as pricing and personalization engines, demand forecasting, and predictive maintenance, taking them from exploration and feature engineering through validation and deployment
Work with client data engineering and analytics teams to acquire, extract, and prepare data for modeling at scale, including cleaning, enriching, de-duping, and joining structured, semi-structured, and unstructured data
Partner with consulting teams to translate client business problems into analytical approaches and help scope the work
Use AI tools with guardrails to accelerate your work, from exploration and prototyping through implementation
Apply generative AI where it offers a better path to a client solution, using techniques such as prompt engineering and retrieval-augmented generation
AI Architect Sr. AdvisorNTT DATA Services · Hyderabad, India
Associate, Full-Stack Data ScientistBain & Company · Santiago+1 more