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Build and deploy enterprise generative AI apps using Azure OpenAI, LangChain, and RAG pipelines; integrate LLMs into microservices and agent workflows.
Builds and maintains backend systems for a SaaS security platform protecting enterprise SaaS apps (e.g., Microsoft 365, Salesforce) via APIs, multithreaded apps, and cloud infrastructure (AWS/GCP). Focuses on threat detection, OAuth token risk management, and scalable distributed systems with AI/ML integration.
Build and deploy AI-enabled applications and agentic workflows for clients, designing the systems around models, tools, and safeguards to ensure reliability in production.
AffBulk росте – і ми шукаємо SEO-спеціаліста, який разом із нами буде досліджувати, як просувати сайти в Google AI Overviews, AI Search та відповідях інших LLM-систем. Це не класична SEO-позиція. Ми шукаємо людину для…
Build and deploy AI-driven content safety systems that protect users from harmful content using ML pipelines, agentic flows, and robust validation gates across Google’s products.
Build and scale the backend platform that powers AI-driven travel experiences, integrating LLMs, vector search, and agentic systems for millions of users.
Lead a team to design, build, and deploy production-grade ML models and AI solutions in cloud environments, translating business needs into scalable data products.
Design enterprise AI solutions using generative agents, RAG, and ITSM/AIOps workflows to automate IT support, diagnostics, and operations with human-in-the-loop oversight.
Senior ML Engineer at Doctolib builds and deploys AI systems (LLMs, retrieval pipelines, agentic AI) to improve patient access to healthcare, using Python, vector databases, and cloud-native tools.
Build and deploy full-stack AI applications for enterprise clients using Google's AI stack, including Gemini models, Vertex AI, and agentic systems, from frontend to production deployment.
At Jellyfish, we believe in the power of diverse perspectives and inclusive collaboration. We welcome individuals who excel in collaborative, varied teams and value the unique contributions that each person brings to…
Build secure, scalable microservices and APIs in Node.js/TypeScript on AWS, own features end-to-end, and use AI tools to accelerate development while keeping systems reliable.
Designs and leads the AI-driven architecture for a security operations platform, integrating threat intelligence, agentic AI, and governed decisioning to modernize enterprise security workflows.
Leads the design and architecture of a production GenAI platform for Deliveroo and DoorDash, focusing on open-weight LLM/VLM serving, fine-tuning, and GPU infrastructure to optimize cost, latency, and scalability for business impact.
ML engineer builds and optimizes RAG pipelines, fine-tunes VLM for technical docs, and prepares on-prem LLM/VLM inference for an AI platform serving engineers.
Lead the architecture of AI-powered data infrastructure for fraud and safety systems, building vector databases, multi-agent orchestration, and proactive guardrails to protect Grab’s platform at scale.
Build and operate the data, features, and GenAI foundations for Human Capital AI products, shipping production pipelines and LLM applications using Claude, GPT, and Gemini-class models with strong governance and observability.
Build and maintain internal AI infrastructure connecting LLMs, agents, and ML systems with company data, tools, and workflows to support autonomy and defense applications.
Lead Security Engineer at JPMorgan Chase designs and implements tamper-proof security solutions for enterprise software, leveraging Java/Python, AI/ML (PyTorch/TensorFlow), GenAI (LLMs, RAG), and threat modeling to harden systems against misuse and vulnerabilities in financial services infrastructure.
Build and deploy AI-agent systems for enterprise clients, owning the full lifecycle from discovery to production using Python, FastAPI, and agent frameworks like Strands.
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