Key skills
AWSAzureCloudFlaskGoogle Cloud PlatformPySparkPythonSQLAIGenAILLMOpenAIClaudeRAGLangChainAgenticMLOpsLangGraphData EngineeringSnowflakeDatabricksFastAPIGCPGoogle CloudCI/CD
About this role
Role Overview
- Design and develop LLM-powered applications using agentic patterns (single/multi-agent) for business use cases
- Build and optimise end-to-end RAG pipelines (ingestion, embeddings, retrieval, orchestration, response synthesis)
- Implement prompt engineering and orchestration techniques (prompt chaining, tool/function calling, structured outputs)
- Develop production-grade APIs and services (FastAPI/Flask/Streamlit) for GenAI applications
- Integrate LLM solutions with enterprise systems, data platforms, and workflows
- Collaborate with Data Engineering and MLOps teams for data pipelines, deployment, monitoring, and scaling
Requirements
- 6–9 years total experience
- 1–3+ years in hands-on GenAI / LLM application development (production use cases)
- 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
- Strong Python/Pyspark engineering expertise (production-grade development)
- Deep data analysis experience and handling large volumes of data
- Experience with Fabric/Azure Databricks/Snowflake data engineering integration skills
- Good exposure to cloud platforms (Azure/AWS/GCP)
- Strong knowledge of SQL
- Familiarity with containers, CI/CD, monitoring
Tech Stack
- AWS
- Azure
- Cloud
- Flask
- Google Cloud Platform
- PySpark
- Python
- SQL
Benefits
- Health insurance
- Flexible working hours
- Professional development
- Paid time off
- Retirement plan