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Develops and maintains generative AI systems (LLMs, AI agents) to enhance insurance services for AIA’s customers, focusing on workflow design, architecture, and performance optimization for business needs.
Build and deploy multi-agent AI systems, multimodal pipelines, and RAG/Graph-RAG for fintech use cases using Python, PyTorch, and Databricks.
Designs and advises clients on secure GenAI or Computer Vision solutions, running discovery workshops and mapping workflows to scalable architectures under high-security constraints.
Build and ship production-grade AI features end-to-end using Python, FastAPI, and LLM APIs on AWS Bedrock, integrating prompt handling, fallbacks, and observability into Guidewire’s insurance platform.
Principal AI Engineer designs and owns the shared AI architecture for multi-agent marketing systems, retrieval pipelines, and evaluation frameworks that power SMB-focused products at scale.
Build and lead enterprise-scale AI platforms using LLMs, RAG, and agentic systems; set standards for reliability, governance, and reuse across teams.
Build and deploy multi-agent AI systems for global financial markets, using Python, FastAPI, and frameworks like LangGraph and CrewAI.
Build AI agents for financial data analysis and trading automation using LLM integration and multi-agent systems in a hedge fund.
Fine-tune and adapt large language models to improve instruction-following and domain-specific performance for production use.
Fine-tunes large language models using instruction fine-tuning and domain adaptation to improve model relevance and performance for specific contexts.
Build and deploy AI models and GenAI agents using Python, Azure, and LLM APIs to automate decisions and enhance financial products in a hybrid role.
Lead AI engineering for generative-AI copilots and RAG systems in a regulated financial-services environment, building production-grade Python/LLM pipelines on Azure.
Build agentic AI workflows in Python to automate corporate IT reliability tasks like incident triage, root-cause analysis, and remediation, using LLMs, RAG, and agent frameworks.
Lead a team of engineers to design, build, and deploy AI and automation solutions for Amgen’s AI Studio, turning business challenges into scalable, production-ready products with measurable impact.
Build and deploy AI-first contract intelligence features using LLMs, RAG, agentic workflows, and knowledge graphs to automate and accelerate deal-making for enterprise customers.
Builds and deploys production-grade agentic AI systems (LLMs, RAG, orchestration) for American Express’s global commercial services, integrating with cloud-native stacks (Go/TypeScript/Python, AWS/GCP, Kafka, Kubernetes) to enhance customer-facing financial solutions.
Build production-ready AI features using Azure OpenAI, RAG pipelines, and MCP agents to enhance SaaS workflows like onboarding and data intake.
Build production-ready GenAI apps and reusable AI assets using RAG, Langchain/Langgraph, and Azure Cloud, while collaborating with cross-functional teams.
Build and scale AI agents and reusable components for automation, using Python, LLM APIs, and cloud-native tools to productionize AI systems and optimize performance.
Build and optimize GPU kernels and inference frameworks (e.g., vLLM) to accelerate large language model serving, integrating research into production-grade, open-source software.
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