Senior Executive - AI Engineer

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

• 0–4 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
  
  

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)

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

• 0–4 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
  
  

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)

• Bachelor’s or Master’s degree in Data Science, Computer Science, AI/ML, Statistics, Mathematics, or a related field.


• 0–4 years of experience in a data science, applied ML, or GenAI role, with a strong portfolio of projects.


• Hands‑on experience with machine learning frameworks (scikit‑learn, TensorFlow, PyTorch).


• Practical experience with LLMs, GenAI frameworks, LangChain, and prompt‑driven workflows.