Senior Executive - AI Engineer

Job description

**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** - **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) **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** - **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) - Qualifications: 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.