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Builds AI-native workflows by designing agent systems for planning, tool integration, and error recovery, bridging frontend, backend, and AI components to create reliable, real-world task automation.
Build and scale ML infrastructure for post-training research on large language models, including compute, scheduling, and evaluation systems using PyTorch/JAX and cloud platforms.
Build and own a structured research data platform for investment teams, ingesting and parsing documents (PDF, HTML, XBRL) and deploying LLM-powered query APIs on cloud data warehouses.
Build and deploy AI agents and RAG pipelines that integrate with banking systems using TypeScript, MCP, and LLM APIs to automate workflows in a regulated digital asset bank.
Join NTT DATA and shape the future of digital innovation At NTT DATA , we are a global technology consulting company helping organizations transform through innovation, technology, and collaboration. With more than…
Responsibilities: Build, deploy, and operate backend systems that power AI-enabled features in production. Design and implement inference pipelines, orchestration layers, and service boundaries around AI models. Ensure…
Westwood Insurance Agency is one of the largest personal lines agencies in the US. Since 1952, we’ve helped more than a million customers protect what matters most. Licensed in all 50 states, Westwood represents…
Build and deploy generative AI solutions in Python, designing backend architectures, RAG systems, and AI agents while implementing DevOps practices on GCP.
Build and maintain scalable data pipelines, lakes, and warehouses in Azure, using Python, SQL, and IaC to enable analytics and reporting for clients.
Designs and optimizes prompts for LLMs (GPT, Claude, Gemini) and builds RAG pipelines using LangChain/LlamaIndex to deliver tailored AI outputs for enterprise domains like legal, medical, and finance.
Designs and deploys production-grade generative AI solutions (LLMs, RAG, AI agents) for enterprise clients, using frameworks like LangChain and cloud platforms like Azure OpenAI, with a focus on scalability, security, and cost efficiency.
Build and scale enterprise microservices in Java and React, then extend them with agentic AI features that securely connect LLMs to internal systems using RAG, MCP, and prompt engineering.
Senior full-stack engineer building Java microservices and AI agents that connect LLMs to enterprise systems using RAG, prompt engineering, and modern AI tooling.
Owns Snowflake as a product at Infor, setting business roadmap, consumption economics, and standards for a shared data platform used by AI agents and analysts.
Build and deploy AI applications and agentic workflows using LangChain/LangGraph, prompt engineering, and Python, while maintaining data pipelines and evaluation frameworks for a fintech firm.
About ChronoAI ChronoAI is an advanced AI technology company specializing in human-centric artificial intelligence solutions. We combine cutting-edge natural language processing with personalized analysis frameworks to…
Design and refine prompts to steer large language models for enterprise use cases like support, content, and operations, collaborating with AI teams to integrate and test them.
Design, build, and deploy AI/ML models, Generative AI apps, and LLM-based solutions using Python, TensorFlow, and PyTorch.
Build and deploy NLP and generative AI systems—fine-tuning LLMs, RAG pipelines, and vector search—to automate document processing and decision support for a mid-market professional services firm.
Configure and deploy Anthropic’s Enterprise Claude platform, integrate Model Context Protocols with N8N workflows, and build a centralised AI skills library while enforcing token efficiency and governance.
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