Associate AI Data Engineer

Job Description: Key Responsibilities

Design and develop LLM-based solutions for business use cases (e.g., chatbots, summarisation, document intelligence).

Build and optimise RAG (Retrieval Augmented Generation) pipelines including data ingestion, embeddings, and retrieval.

Implement prompt engineering techniques (prompt design, chaining, optimisation).

Develop backend services/APIs for AI applications using Python frameworks (FastAPI / Flask / Streamlit) .

Integrate LLM solutions with enterprise systems and structured/unstructured data sources.

Apply basic guardrails and evaluation techniques to improve response quality and reduce hallucinations.

Collaborate with cross-functional teams to ensure data quality, model performance, and deployment readiness .

Document solutions and contribute to reusable components and best practices.

Must-Have Skills

Experience

2–4 years total experience , with exposure to AI/ML, NLP, or Data Engineering projects

Hands-on experience or strong learning exposure to LLM / GenAI use cases (projects, POCs, academic work, or professional)

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

Exposure to agentic workflows or tool calling concepts

Basic knowledge of fine-tuning / prompt tuning (LoRA, PEFT – optional exposure)

Experience with Azure OpenAI / Azure AI Search or similar stacks

Awareness of enterprise AI considerations (data security, privacy, governance)

Responsibilities: Key Responsibilities

Design and develop LLM-based solutions for business use cases (e.g., chatbots, summarisation, document intelligence).

Build and optimise RAG (Retrieval Augmented Generation) pipelines including data ingestion, embeddings, and retrieval.

Implement prompt engineering techniques (prompt design, chaining, optimisation).

Develop backend services/APIs for AI applications using Python frameworks (FastAPI / Flask / Streamlit) .

Integrate LLM solutions with enterprise systems and structured/unstructured data sources.

Apply basic guardrails and evaluation techniques to improve response quality and reduce hallucinations.

Collaborate with cross-functional teams to ensure data quality, model performance, and deployment readiness .

Document solutions and contribute to reusable components and best practices.

Must-Have Skills

Experience

2–4 years total experience , with exposure to AI/ML, NLP, or Data Engineering projects

Hands-on experience or strong learning exposure to LLM / GenAI use cases (projects, POCs, academic work, or professional)

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

Exposure to agentic workflows or tool calling concepts

Basic knowledge of fine-tuning / prompt tuning (LoRA, PEFT – optional exposure)

Experience with Azure OpenAI / Azure AI Search or similar stacks

Awareness of enterprise AI considerations (data security, privacy, governance)

Qualifications: Key Responsibilities

Design and develop LLM-based solutions for business use cases (e.g., chatbots, summarisation, document intelligence).

Build and optimise RAG (Retrieval Augmented Generation) pipelines including data ingestion, embeddings, and retrieval.

Implement prompt engineering techniques (prompt design, chaining, optimisation).

Develop backend services/APIs for AI applications using Python frameworks (FastAPI / Flask / Streamlit) .

Integrate LLM solutions with enterprise systems and structured/unstructured data sources.

Apply basic guardrails and evaluation techniques to improve response quality and reduce hallucinations.

Collaborate with cross-functional teams to ensure data quality, model performance, and deployment readiness .

Document solutions and contribute to reusable components and best practices.

Must-Have Skills

Experience

2–4 years total experience , with exposure to AI/ML, NLP, or Data Engineering projects

Hands-on experience or strong learning exposure to LLM / GenAI use cases (projects, POCs, academic work, or professional)

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

Exposure to agentic workflows or tool calling concepts

Basic knowledge of fine-tuning / prompt tuning (LoRA, PEFT – optional exposure)

Experience with Azure OpenAI / Azure AI Search or similar stacks

Awareness of enterprise AI considerations (data security, privacy, governance)