Senior AI Data Engineer
Gurugram, Haryana, India
- Pay
- Salary not listed in the saved posting
- Work setup
- Unconfirmed
- Employment
- Unconfirmed
What you’ll work on
Full postingDesign and develop LLM-powered applications using agentic patterns (single/multi-agent) for business use cases
Build and optimise end-to-end RAG pipelines (ingestion, embeddings, retrieval, orchestration, response synthesis)
Implement prompt engineering and orchestration techniques (prompt chaining, tool/function calling, structured outputs)
From the employer’s posting
Key Responsibilities Design and develop LLM-powered applications using agentic patterns (single/multi-agent) for business use cases Build and optimise end-to-end RAG pipelines (ingestion, embeddings, retrieval, orchestration, response synthesis)
Design and develop LLM-powered applications using agentic patterns (single/multi-agent) for business use cases Build and optimise end-to-end RAG pipelines (ingestion, embeddings, retrieval, orchestration, response synthesis) Implement prompt engineering and orchestration techniques (prompt chaining, tool/function calling, structured outputs)
Build and optimise end-to-end RAG pipelines (ingestion, embeddings, retrieval, orchestration, response synthesis) Implement prompt engineering and orchestration techniques (prompt chaining, tool/function calling, structured outputs) Develop production-grade APIs and services (FastAPI/Flask/Streamlit) for GenAI applications
Tools in this posting
- Azure
- SQL
- Python
- AWS
- Google Cloud (GCP)
- Snowflake
- Databricks
- PySpark
- Fastapi
- Flask
- Streamlit
Source — Tool mentions in context
- Deep data analysis experience and handling large volume of data - Fabric/Azure Databricks/Snowflake data engineering integration skills - Good exposure to:
- Good exposure to: - Cloud platforms (Azure/AWS/GCP) - SQL
- Experience with enterprise GenAI security & privacy practices (data masking, access control, compliance) - Familiarity with Azure AI ecosystem (Azure OpenAI, Azure AI Search, Fabric, etc.) Exposure to agentic coding tools (e.g., Claude Code or similar environments)
- Cloud platforms (Azure/AWS/GCP) - SQL - Containers, CI/CD, monitoring
Core Engineering - Strong Python/Pyspark engineering expertise (production-grade development) with proven API integration experience - Deep data analysis experience and handling large volume of data
- Implement prompt engineering and orchestration techniques (prompt chaining, tool/function calling, structured outputs) - Develop production-grade APIs and services (FastAPI/Flask/Streamlit) for GenAI applications - Integrate LLM solutions with enterprise systems, data platforms, and workflows
Job description
Key Responsibilities
- Design and develop LLM-powered applications using agentic patterns (single/multi-agent) for business use cases
- Build and optimise end-to-end RAG pipelines (ingestion, embeddings, retrieval, orchestration, response synthesis)
- Implement prompt engineering and orchestration techniques (prompt chaining, tool/function calling, structured outputs)
- Develop production-grade APIs and services (FastAPI/Flask/Streamlit) for GenAI applications
- Integrate LLM solutions with enterprise systems, data platforms, and workflows
- Apply guardrails and evaluation frameworks to improve response quality, reduce hallucinations, and ensure responsible AI usage
- Collaborate with Data Engineering and MLOps teams for data pipelines, deployment, monitoring, and scaling
- Contribute to reusable components, documentation, and engineering best practices
Experience & Core Requirements (Must-Have)
Overall Experience
- 6–9 years total experience
- 1–3+ years in hands-on GenAI / LLM application development (production use cases)
LLM / GenAI & Agentic Engineering
- Strong hands-on experience with:
- LLMs (Claude, OpenAI, etc.)
- RAG pipelines and retrieval optimisation
- GPT + Agentic AI implementation experience
- Experience with:
- LangChain, LangGraph, or similar frameworks
- Agent orchestration and tool-calling architectures
- Deep understanding of:
- LLM limitations, evaluation, and optimisation strategies
Core Engineering
- Strong Python/Pyspark engineering expertise (production-grade development) with proven API integration experience
- Deep data analysis experience and handling large volume of data
- Fabric/Azure Databricks/Snowflake data engineering integration skills
- Good exposure to:
- Cloud platforms (Azure/AWS/GCP)
- SQL
- Containers, CI/CD, monitoring
Data / AI Foundations (Mandatory)
Prior experience in one or more:
- Data Engineering (ETL/ELT, pipelines, orchestration)
- Data Science / ML lifecycle (especially NLP)
- Analytics engineering / data products
Good-to-Have / Preferred
- Experience with fine-tuning techniques (LoRA, PEFT) or prompt tuning strategies
- Experience with enterprise GenAI security & privacy practices (data masking, access control, compliance)
- Familiarity with Azure AI ecosystem (Azure OpenAI, Azure AI Search, Fabric, etc.)
Exposure to agentic coding tools (e.g., Claude Code or similar environments)
Key Responsibilities
- Design and develop LLM-powered applications using agentic patterns (single/multi-agent) for business use cases
- Build and optimise end-to-end RAG pipelines (ingestion, embeddings, retrieval, orchestration, response synthesis)
- Implement prompt engineering and orchestration techniques (prompt chaining, tool/function calling, structured outputs)
- Develop production-grade APIs and services (FastAPI/Flask/Streamlit) for GenAI applications
- Integrate LLM solutions with enterprise systems, data platforms, and workflows
- Apply guardrails and evaluation frameworks to improve response quality, reduce hallucinations, and ensure responsible AI usage
- Collaborate with Data Engineering and MLOps teams for data pipelines, deployment, monitoring, and scaling
- Contribute to reusable components, documentation, and engineering best practices
Experience & Core Requirements (Must-Have)
Overall Experience
- 6–9 years total experience
- 1–3+ years in hands-on GenAI / LLM application development (production use cases)
LLM / GenAI & Agentic Engineering
- Strong hands-on experience with:
- LLMs (Claude, OpenAI, etc.)
- RAG pipelines and retrieval optimisation
- GPT + Agentic AI implementation experience
- Experience with:
- LangChain, LangGraph, or similar frameworks
- Agent orchestration and tool-calling architectures
- Deep understanding of:
- LLM limitations, evaluation, and optimisation strategies
Core Engineering
- Strong Python/Pyspark engineering expertise (production-grade development) with proven API integration experience
- Deep data analysis experience and handling large volume of data
- Fabric/Azure Databricks/Snowflake data engineering integration skills
- Good exposure to:
- Cloud platforms (Azure/AWS/GCP)
- SQL
- Containers, CI/CD, monitoring
Data / AI Foundations (Mandatory)
Prior experience in one or more:
- Data Engineering (ETL/ELT, pipelines, orchestration)
- Data Science / ML lifecycle (especially NLP)
- Analytics engineering / data products
Good-to-Have / Preferred
- Experience with fine-tuning techniques (LoRA, PEFT) or prompt tuning strategies
- Experience with enterprise GenAI security & privacy practices (data masking, access control, compliance)
- Familiarity with Azure AI ecosystem (Azure OpenAI, Azure AI Search, Fabric, etc.)
Exposure to agentic coding tools (e.g., Claude Code or similar environments)
Key Responsibilities
- Design and develop LLM-powered applications using agentic patterns (single/multi-agent) for business use cases
- Build and optimise end-to-end RAG pipelines (ingestion, embeddings, retrieval, orchestration, response synthesis)
- Implement prompt engineering and orchestration techniques (prompt chaining, tool/function calling, structured outputs)
- Develop production-grade APIs and services (FastAPI/Flask/Streamlit) for GenAI applications
- Integrate LLM solutions with enterprise systems, data platforms, and workflows
- Apply guardrails and evaluation frameworks to improve response quality, reduce hallucinations, and ensure responsible AI usage
- Collaborate with Data Engineering and MLOps teams for data pipelines, deployment, monitoring, and scaling
- Contribute to reusable components, documentation, and engineering best practices
Experience & Core Requirements (Must-Have)
Overall Experience
- 6–9 years total experience
- 1–3+ years in hands-on GenAI / LLM application development (production use cases)
LLM / GenAI & Agentic Engineering
- Strong hands-on experience with:
- LLMs (Claude, OpenAI, etc.)
- RAG pipelines and retrieval optimisation
- GPT + Agentic AI implementation experience
- Experience with:
- LangChain, LangGraph, or similar frameworks
- Agent orchestration and tool-calling architectures
- Deep understanding of:
- LLM limitations, evaluation, and optimisation strategies
Core Engineering
- Strong Python/Pyspark engineering expertise (production-grade development) with proven API integration experience
- Deep data analysis experience and handling large volume of data
- Fabric/Azure Databricks/Snowflake data engineering integration skills
- Good exposure to:
- Cloud platforms (Azure/AWS/GCP)
- SQL
- Containers, CI/CD, monitoring
Data / AI Foundations (Mandatory)
Prior experience in one or more:
- Data Engineering (ETL/ELT, pipelines, orchestration)
- Data Science / ML lifecycle (especially NLP)
- Analytics engineering / data products
Good-to-Have / Preferred
- Experience with fine-tuning techniques (LoRA, PEFT) or prompt tuning strategies
- Experience with enterprise GenAI security & privacy practices (data masking, access control, compliance)
- Familiarity with Azure AI ecosystem (Azure OpenAI, Azure AI Search, Fabric, etc.)
Exposure to agentic coding tools (e.g., Claude Code or similar environments)
Your next step
- Have your CV and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the salary range before committing time to the process.
Complete your application on fa-ewjt-saasfaprod1.fa.ocs.oraclecloud.com. The employer’s form will show what is required.
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Source & posting history
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Gurugram, Haryana, India
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
- Status in our records
- Active
- First seen by us
- Aug 12, 2026
- Recorded sightings
- 168
- Last seen by us
- Oct 7, 2026
- Employer says posted
- Jul 8, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
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