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Tech Stack
Tools & technologiesAWSAzureCloudETLFlaskGoogle Cloud PlatformPySparkPythonSQL
About the role
Key responsibilities & impact- Design and develop LLM-based applications using single-agent or simple multi-agent patterns for business use cases
- Build and maintain RAG pipelines: data ingestion → chunking → embeddings → retrieval → response generation
- Implement prompt engineering techniques (prompt templates, chaining, basic tool/function calling)
- Develop backend services/APIs for AI applications using Python frameworks (FastAPI / Flask / Streamlit)
- Integrate AI solutions with enterprise systems, databases, and APIs
- Apply basic guardrails and validation checks to improve response quality and reduce hallucination
- Work with Data Engineering teams to ensure data quality, pipeline efficiency, and proper documentation
- Collaborate with MLOps teams for deployment, monitoring, and iterative improvements
- Document solutions, reusable components, and best practices
Requirements
What you’ll need- 4–6 years total experience
- 1+ year hands-on experience in GenAI / LLM-based applications
- Strong hands-on experience with LLMs (Claude, OpenAI, etc.)
- Experience with RAG pipelines and retrieval optimisation
- Experience with GPT + Agentic AI implementation
- Experience with LangChain, LangGraph, or similar frameworks
- Deep understanding of LLM limitations, evaluation, and optimisation strategies
- Strong Python/Pyspark engineering expertise
- 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)
- Good exposure to SQL
- Good exposure to Containers, CI/CD, monitoring
- Data Engineering (ETL/ELT, pipelines, orchestration)
- Data Science / ML lifecycle (especially NLP)
- Analytics engineering / data products
- Exposure to model fine-tuning (LoRA/PEFT) or prompt optimisation techniques
- Experience with evaluation of LLM outputs (quality, relevance, latency)
- Understanding of enterprise data privacy and security considerations in GenAI
- Exposure to Azure AI / Azure OpenAI / AI Search ecosystems
- Experience working on real client-facing AI solutions or POCs.
Benefits
Comp & perks- Health insurance
- 401(k) matching
- Flexible work hours
- Paid time off
- Remote work options
ATS Keywords
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Hard Skills & Tools
LLM (Claude, OpenAI)RAG PipelinesPythonPysparkSQLData AnalysisETL/ELTNLPModel Fine-TuningPrompt Engineering