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.