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Designs and governs data architecture for global engineering platforms, analytics, and AI initiatives, creating reusable data models and integration patterns to improve engineering productivity and product lifecycle visibility.
Build and scale LLM-based solutions, including agent orchestration, RAG pipelines, and cloud infrastructure, using Python, PySpark, and AWS services.
Design and build production-grade AI systems using generative models, RAG, and cloud platforms (Azure/AWS/GCP) to solve real business problems with scalable, maintainable solutions.
Build and deploy production-grade GenAI systems using LLMs, RAG, and agent frameworks in Python/PySpark on AWS, optimizing for reliability and scale.
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…
Line of Service Assurance Industry/Sector FS X-Sector Specialism Assurance Management Level Associate Job Description & Summary At PwC, we help clients build trust and reinvent so they can turn complexity into…
Senior AI Engineer - LLM Agents Patsnap's Materials team builds AI systems that help R&D scientists and engineers search, extract, and reason over materials science and patent data. You will own the agentic…
About The Team Shopee will be prioritizing applicants who have a current right to work in Singapore, and do not require Shopee sponsorship of a visa. Kindly note that you can only be considered in one recruitment…
Develops an agentic AI framework (JARVIS) for pharma use cases, designing Python-based LLM workflows, Streamlit interfaces, and Kubernetes deployments to validate technical feasibility for a future pilot.
Lead end-to-end data and AI engineering for production-grade GenAI/ML solutions, from data architecture and MLOps to LLMs, RAG, and prompt engineering, in a global consultancy.
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.
Build and deploy ML tools (LLMs, multimodal, GenAI) to automate engineering workflows for an electric flying-taxi startup.
Build and improve AI agents that automate cybersecurity workflows like SOC alert triage, threat hunting, and penetration testing using Python, LLMs, and distributed systems.
Builds enterprise AI apps using LLMs, RAG, and Agentic AI frameworks in Python, deploys them on cloud platforms, and integrates them with enterprise systems.
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.
Builds and owns AI-driven agent systems for workflow automation, integrating LLMs, RAG, and agentic pipelines to optimize enterprise processes—from prototyping to production deployment.
Senior full-stack engineer building Java microservices and AI agents that connect LLMs to enterprise systems using RAG, prompt engineering, and modern AI tooling.
Builds and deploys AI systems using Python, RAG, and NLP to power generative AI solutions for clients, working on-site in Amsterdam.
Lead a team building production-grade generative AI and LLM solutions for healthcare, focusing on multi-agent workflows, RAG, and cloud-native deployment.
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