Job description

Key Responsibilities

Design and develop LLM-based applications using single-agent or simple multi-agent patterns for business use cases
Build and maintain RAG pipelines: data ingestion → chunking → embeddings → retrieval → response generation
Implement prompt engineering techniques (prompt templates, chaining, basic tool/function calling)
Develop backend services/APIs for AI applications using Python frameworks (FastAPI / Flask / Streamlit)
Integrate AI solutions with enterprise systems, databases, and APIs
Apply basic guardrails and validation checks to improve response quality and reduce hallucination
Work with Data Engineering teams to ensure data quality, pipeline efficiency, and proper documentation
Collaborate with MLOps teams for deployment, monitoring, and iterative improvements
Document solutions, reusable components, and best practices

Must-Have Skills

Experience

4–6 years total experience, with 1+ year hands-on experience in GenAI / LLM-based applications

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

Exposure to model fine-tuning (LoRA/PEFT) or prompt optimisation techniques
Experience with evaluation of LLM outputs (quality, relevance, latency)
Understanding of enterprise data privacy and security considerations in GenAI
Exposure to Azure AI / Azure OpenAI / AI Search ecosystems
Experience working on real client-facing AI solutions or POCs

Key Responsibilities

Design and develop LLM-based applications using single-agent or simple multi-agent patterns for business use cases
Build and maintain RAG pipelines: data ingestion → chunking → embeddings → retrieval → response generation
Implement prompt engineering techniques (prompt templates, chaining, basic tool/function calling)
Develop backend services/APIs for AI applications using Python frameworks (FastAPI / Flask / Streamlit)
Integrate AI solutions with enterprise systems, databases, and APIs
Apply basic guardrails and validation checks to improve response quality and reduce hallucination
Work with Data Engineering teams to ensure data quality, pipeline efficiency, and proper documentation
Collaborate with MLOps teams for deployment, monitoring, and iterative improvements
Document solutions, reusable components, and best practices

Must-Have Skills

Experience

4–6 years total experience, with 1+ year hands-on experience in GenAI / LLM-based applications

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

Exposure to model fine-tuning (LoRA/PEFT) or prompt optimisation techniques
Experience with evaluation of LLM outputs (quality, relevance, latency)
Understanding of enterprise data privacy and security considerations in GenAI
Exposure to Azure AI / Azure OpenAI / AI Search ecosystems
Experience working on real client-facing AI solutions or POCs

Key Responsibilities

Design and develop LLM-based applications using single-agent or simple multi-agent patterns for business use cases
Build and maintain RAG pipelines: data ingestion → chunking → embeddings → retrieval → response generation
Implement prompt engineering techniques (prompt templates, chaining, basic tool/function calling)
Develop backend services/APIs for AI applications using Python frameworks (FastAPI / Flask / Streamlit)
Integrate AI solutions with enterprise systems, databases, and APIs
Apply basic guardrails and validation checks to improve response quality and reduce hallucination
Work with Data Engineering teams to ensure data quality, pipeline efficiency, and proper documentation
Collaborate with MLOps teams for deployment, monitoring, and iterative improvements
Document solutions, reusable components, and best practices

Must-Have Skills

Experience

4–6 years total experience, with 1+ year hands-on experience in GenAI / LLM-based applications

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

Exposure to model fine-tuning (LoRA/PEFT) or prompt optimisation techniques
Experience with evaluation of LLM outputs (quality, relevance, latency)
Understanding of enterprise data privacy and security considerations in GenAI
Exposure to Azure AI / Azure OpenAI / AI Search ecosystems
Experience working on real client-facing AI solutions or POCs

Someone shared one job with you. ASAI keeps finding the rest.

Scored, not searched. Every role ranked against your actual profile.

Alerts as often as hourly. Reach new roles while the pile is still small.

Skill gaps, spelled out. See exactly which requirements you don't meet yet.

Verified jobs, only. Say no to ghost jobs. Your time deserves respect.

Someone shared one job with you. ASAI keeps finding the rest.

Scored, not searched. Every role ranked against your actual profile.

Alerts as often as hourly. Reach new roles while the pile is still small.

Skill gaps, spelled out. See exactly which requirements you don't meet yet.

Verified jobs, only. Say no to ghost jobs. Your time deserves respect.

Job description

Key Responsibilities

Design and develop LLM-based applications using single-agent or simple multi-agent patterns for business use cases
Build and maintain RAG pipelines: data ingestion → chunking → embeddings → retrieval → response generation
Implement prompt engineering techniques (prompt templates, chaining, basic tool/function calling)
Develop backend services/APIs for AI applications using Python frameworks (FastAPI / Flask / Streamlit)
Integrate AI solutions with enterprise systems, databases, and APIs
Apply basic guardrails and validation checks to improve response quality and reduce hallucination
Work with Data Engineering teams to ensure data quality, pipeline efficiency, and proper documentation
Collaborate with MLOps teams for deployment, monitoring, and iterative improvements
Document solutions, reusable components, and best practices

Must-Have Skills

Experience

4–6 years total experience, with 1+ year hands-on experience in GenAI / LLM-based applications

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

Exposure to model fine-tuning (LoRA/PEFT) or prompt optimisation techniques
Experience with evaluation of LLM outputs (quality, relevance, latency)
Understanding of enterprise data privacy and security considerations in GenAI
Exposure to Azure AI / Azure OpenAI / AI Search ecosystems
Experience working on real client-facing AI solutions or POCs

Key Responsibilities

Design and develop LLM-based applications using single-agent or simple multi-agent patterns for business use cases
Build and maintain RAG pipelines: data ingestion → chunking → embeddings → retrieval → response generation
Implement prompt engineering techniques (prompt templates, chaining, basic tool/function calling)
Develop backend services/APIs for AI applications using Python frameworks (FastAPI / Flask / Streamlit)
Integrate AI solutions with enterprise systems, databases, and APIs
Apply basic guardrails and validation checks to improve response quality and reduce hallucination
Work with Data Engineering teams to ensure data quality, pipeline efficiency, and proper documentation
Collaborate with MLOps teams for deployment, monitoring, and iterative improvements
Document solutions, reusable components, and best practices

Must-Have Skills

Experience

4–6 years total experience, with 1+ year hands-on experience in GenAI / LLM-based applications

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

Exposure to model fine-tuning (LoRA/PEFT) or prompt optimisation techniques
Experience with evaluation of LLM outputs (quality, relevance, latency)
Understanding of enterprise data privacy and security considerations in GenAI
Exposure to Azure AI / Azure OpenAI / AI Search ecosystems
Experience working on real client-facing AI solutions or POCs

Key Responsibilities

Design and develop LLM-based applications using single-agent or simple multi-agent patterns for business use cases
Build and maintain RAG pipelines: data ingestion → chunking → embeddings → retrieval → response generation
Implement prompt engineering techniques (prompt templates, chaining, basic tool/function calling)
Develop backend services/APIs for AI applications using Python frameworks (FastAPI / Flask / Streamlit)
Integrate AI solutions with enterprise systems, databases, and APIs
Apply basic guardrails and validation checks to improve response quality and reduce hallucination
Work with Data Engineering teams to ensure data quality, pipeline efficiency, and proper documentation
Collaborate with MLOps teams for deployment, monitoring, and iterative improvements
Document solutions, reusable components, and best practices

Must-Have Skills

Experience

4–6 years total experience, with 1+ year hands-on experience in GenAI / LLM-based applications

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

Exposure to model fine-tuning (LoRA/PEFT) or prompt optimisation techniques
Experience with evaluation of LLM outputs (quality, relevance, latency)
Understanding of enterprise data privacy and security considerations in GenAI
Exposure to Azure AI / Azure OpenAI / AI Search ecosystems
Experience working on real client-facing AI solutions or POCs