Associate AI Data Engineer
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Key skills for this role
Key Skills for This Role
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Key Responsibilities
- Design and develop LLM-based solutions for chatbots, summarization, document intelligence, and other business use cases.
- Build and optimize RAG pipelines covering data ingestion, embeddings, and retrieval.
- Implement prompt design, chaining, and optimization techniques.
- Develop backend services and APIs for AI applications using FastAPI, Flask, Streamlit, or similar frameworks.
- Integrate LLM solutions with enterprise systems and structured or unstructured data sources.
- Apply guardrails and evaluation techniques to improve response quality and reduce hallucinations.
- Collaborate across teams on data quality, model performance, and deployment readiness.
- Document solutions and contribute to reusable components and best practices.
Experience
- 2–4 years of total experience with exposure to AI/ML, NLP, or data engineering projects.
- Hands-on experience or strong learning exposure to LLM or generative AI use cases through projects, proofs of concept, academic work, or professional work.
LLM and Agentic Engineering
- Strong hands-on experience with LLMs such as Claude or OpenAI models.
- Experience with RAG pipelines, retrieval optimization, GPT-based implementations, and agentic AI.
- Experience with LangChain, LangGraph, or similar frameworks.
- Experience with agent orchestration and tool-calling architectures.
- Deep understanding of LLM limitations, evaluation, and optimization strategies.
Core Engineering
- Strong Python or PySpark engineering expertise with production-grade development and API integration.
- Deep data analysis experience and ability to handle large volumes of data.
- Data engineering integration skills with Fabric, Azure Databricks, Snowflake, or equivalent platforms.
- Exposure to Azure, AWS, or GCP, plus SQL, containers, CI/CD, and monitoring.
Data and AI Foundations
- Prior experience in data engineering, data science or ML lifecycle work, or analytics engineering and data products is mandatory.
Good-to-Have Skills
- Exposure to agentic workflows or tool-calling concepts is preferred.
- Basic knowledge of fine-tuning or prompt tuning, including LoRA or PEFT, is optional.
- Experience with Azure OpenAI, Azure AI Search, or similar stacks is preferred.
- Awareness of enterprise AI considerations such as data security, privacy, and governance is preferred.
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