Bigdara Developer
Tech Stack & Platform Overview
The solution is built for scalable, enterprise-grade Agentic AI solutions, enabling efficient data ingestion, processing, and delivery to AI agents.
Key components include:
Years of Experience- 3 to 5 Years
Mandatory Technical Skills
Python, Google Cloud Platform
Core data ingestion and transformation pipelines
Data layers supporting RAG workflows and AI agents
LLM observability and logging data pipelines
AgentOps-related operational data pipelines
Integration with agent frameworks such as CrewAI, AutoGen, and LangGraph
A modular, cloud-native architecture for rapid iteration and deployment
Essential functions
Nice-to-Have Requirements
Hands-on experience designing, building, and deploying data pipelines
Experience with ETL/ELT pipelines for structured and unstructured data
Familiarity with batch and streaming data processing
Understanding of data quality, monitoring, and reliability practices
Exposure to cloud-based data platforms and deployment workflows
Basic understanding of how data pipelines support AI/LLM or RAG use cases
Ability to collaborate with AI engineers, platform engineers, and product teams
Willingness to learn and adapt in a fast-paced environment3.
Qualifications
Tech Stack & Platform Overview
The solution is built for scalable, enterprise-grade Agentic AI solutions, enabling efficient data ingestion, processing, and delivery to AI agents.
Key components include:
Years of Experience- 3 to 5 Years
Mandatory Technical Skills
Python, Google Cloud Platform
Core data ingestion and transformation pipelines
Data layers supporting RAG workflows and AI agents
LLM observability and logging data pipelines
AgentOps-related operational data pipelines
Integration with agent frameworks such as CrewAI, AutoGen, and LangGraph
A modular, cloud-native architecture for rapid iteration and deployment
Would be a plus
Nice-to-Have Requirements
Hands-on experience designing, building, and deploying data pipelines
Experience with ETL/ELT pipelines for structured and unstructured data
Familiarity with batch and streaming data processing
Understanding of data quality, monitoring, and reliability practices
Exposure to cloud-based data platforms and deployment workflows
Basic understanding of how data pipelines support AI/LLM or RAG use cases
Ability to collaborate with AI engineers, platform engineers, and product teams
Willingness to learn and adapt in a fast-paced environment3.
We offer
- Opportunity to work on bleeding-edge projects
- Work with a highly motivated and dedicated team
- Competitive salary
- Flexible schedule
- Benefits package - medical insurance, sports
- Corporate social events
- Professional development opportunities
- Well-equipped office
About us
Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud & DevOps, application modernization and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.Apply to the position
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