- As an AI ML Engineer you will help build and implement the AI capabilities that power our agentic AI platform
- You will work closely with AI ML architects founding team data engineers and platform engineers to develop agent workflows retrieval pipelines evaluation routines and production ready AI components
- This is a hands on engineering role for someone who is comfortable building with modern AI ML and LLM frameworks agentic AI systems experimenting with models and prompts and translating design patterns into working software
- The role is ideal for an engineer who wants to work on real world enterprise AI systems rather than isolated demos
- Build and enhance AI agent workflows using frameworks such as LangGraph LangChain AutoGen CrewAI or equivalent technologies
- Implement AI agents for data discovery profiling enrichment extraction classification contextual reasoning and enterprise knowledge discovery
- Develop retrieval augmented generation pipelines including document chunking embedding generation metadata tagging vector indexing retrieval tuning and response generation
- Work with vector databases such as Pinecone Milvus Qdrant Weaviate pgvector Chroma or equivalent technologies
- Build structured data agents that can connect to databases inspect schemas generate SQL and support semantic understanding of enterprise data
- Implement document intelligence workflows for PDFs Word documents emails transcripts logs and semi structured enterprise content using Azure Document Intelligence AWS Textract LlamaParse or equivalent tools
- Support prompt engineering prompt testing prompt versioning and reusable prompt template development
- Implement AI evaluation routines covering answer quality retrieval quality hallucination checks regression tests robustness checks and response consistency
- Support integration with LLM APIs and open source models from providers such as OpenAI Anthropic Azure OpenAI Hugging Face Llama Gemma or equivalent ecosystems
- Strong hands on programming experience in Python and building AI agents
- Practical exposure to LLMs RAG vector databases embedding models prompt engineering and AI application development
- Experience with one or more AI LLM frameworks such as LangChain LangGraph LlamaIndex AutoGen CrewAI Semantic Kernel or equivalent tools
- Working knowledge of REST APIs microservices Git based development unit testing and modern software engineering practices
- Familiarity with SQL and structured data concepts including tables schemas joins relationships and query generation
Technology->AI-AI Engineering->LLMOps,Technology->AI-Data science->Machine Learning,Technology->AI-Data science->PYTHON
