Senior AI Data Engineer
Reads like a ML Engineer role: putting models into production.
Gurugram, Haryana, India
- Pay
- Pay not listed
- Work setting
Hybrid — work setting source
Work setup in the employer’s posting: Hybrid
From the employer’s posting- Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting
What you’ll work on
Full postingDesign and develop LLM-powered applications using single or multi-agent patterns
Build and optimise end-to-end RAG pipelines, from ingestion to response synthesis
Develop production-grade APIs and services for GenAI applications
Integrate LLM solutions with enterprise systems, data platforms, and workflows
Apply guardrails and evaluation frameworks to reduce hallucinations
From the employer’s posting
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)
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
Tools in this posting
- Python
- SQL
- AWS
- Azure
- Databricks
- GCP
- Snowflake
- FastAPI
- Flask
- PySpark
- Streamlit
Source — Tool mentions in context
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
- Cloud platforms (Azure/AWS/GCP) - SQL - Containers, CI/CD, monitoring
- Good exposure to: - Cloud platforms (Azure/AWS/GCP) - SQL
- Deep data analysis experience and handling large volume of data - Fabric/Azure Databricks/Snowflake data engineering integration skills - Good exposure to:
- 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)
- 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
Posting record
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- Live
- First seen by us
- Aug 12, 2026 · 78 days ago
- Recorded sightings
- 331
- Last seen by us
- Oct 29, 2026
- Employer says posted
- Jul 8, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
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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)
Source details
Source notes
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- 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.
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