Generative AI & LLMs: Hands-on experience building production LLM applications and designing agentic systems: prompt and context engineering, retrieval-augmented generation (including graph-based retrieval), tool use, state and control flow design, orchestration frameworks, and the tracing, guardrails, and evaluations that make them reliable. Basic knowledge of fine-tuning, parameter-efficient adaptation, dataset construction, and evaluation.
Production Engineering: Translate messy, real-world financial records (invoices, bank statements, contracts, ledgers) into structured data via OCR, NLP, and agentic workflows.
Enhance and maintain user interfaces for usability and continuous improvement across various Channels including Voice/Telephony, Web Messaging, Mobile Applications, and WhatsApp
Data & Analytics Integration: Integrate agent workflows with FAM 2.0 data and analytics components, including Redshift, Glue and PySpark. Build and maintain data preparation and feature-engineering processes required to support AI and ML workloads.
Agentic AI Architecture: Design the end-to-end architecture for FAM agentic AI solutions, including agent orchestration, workflow design, state management, tool integration, human approval points and system interactions, using suitable frameworks such as LangGraph or CrewAI. Architecture decisions are subject to consultation with DSAD’s Platform Team.