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.