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Build and own AI agent infrastructure and automation systems that integrate LLMs with internal tools and data platforms to streamline marketing operations at scale.
ENIOR FULLSTACK & AI/ML ENGINEER We're looking for a Senior Fullstack & AI/ML Engineer to implement AI solutions that automate operational business processes. We need someone who can own solutions…
Build and scale backend services in Java/Spring Boot while integrating AI tools to enhance engineering workflows and product capabilities for a fast-growing SaaS workforce management platform.
Builds and deploys production-grade AI systems using LangChain/LangGraph, focusing on RAG, agentic workflows, and LLM-powered applications. Translates AI architectures into scalable, observable, and secure cloud-native solutions with Kubernetes, vector databases, and observability tools.
Senior Forward Deployed Software Engineer on ServiceNow's Applied AI team, building and deploying production LLM-powered applications end-to-end for strategic enterprise customers in London, spanning backend services, orchestration pipelines, and front-end integrations.
Design and ship AI-powered tools, autonomous agents, and LLM-driven features for ICON's construction technology platform, building agentic AI systems using TypeScript/Python, RAG pipelines, and multi-agent frameworks.
Build and deploy AI agents and LLM-powered tools to automate workflows for ICON's construction technology platform, using Python/TypeScript and modern AI frameworks.
Build production-grade, tool-augmented AI agents using LLMs, RAG, and agent orchestration frameworks, integrating them with enterprise systems and enforcing safety policies.
Designs and builds production-grade AI agents for enterprise revenue workflows, combining deep ML expertise with hands-on agent architecture, reasoning loops, and guardrails in a hybrid Bangalore role.
Build and scale dunnhumby’s Enterprise AI Platform, designing production-grade AI systems including RAG, agentic workflows, and LLM-powered services using Python, LangChain, and cloud-native tools.
Senior hands-on role designing and delivering scalable data and AI solutions—data pipelines, LLM/RAG integrations, and distributed data systems—translating architectural requirements into HLD/LLD specs and leading delivery teams in a hybrid setup based in Pune.
Staff Data Engineer building an end-to-end data platform from scratch, designing near real-time streaming pipelines, vector database infrastructure, and AI-ready data systems using Kafka, dbt, cloud warehouses, and Python.
Design and deploy LLM-powered features, build RAG pipelines, and implement agentic workflows using Python and Azure, focusing on evaluation and integration with minimal supervision.
Agentic AI Engineer building AI agent solutions using Python, FastAPI, vector databases, LLMs, RAG, and LangChain/LangGraph on GCP.
Lead enterprise-scale GenAI implementations for Fortune 500 clients, designing and building production-grade LLM, RAG, and agent-based systems using Python, cloud platforms (AWS/Azure/GCP), and frameworks like LangChain.
The AI Engineer will design, develop, and deploy AI-powered solutions, focusing on Generative AI, LLMs, and MLOps. The role involves building scalable applications using Python and integrating them into enterprise systems via APIs.
At Signature Aviation, we are modernizing operations and customer experiences through advanced data platforms and artificial intelligence. We are seeking a Principal Engineer – AI Agentic Systems to design, architect,…
Architect and build production-grade GenAI systems including RAG pipelines, LLM integrations, and agentic applications using Python, FastAPI, vector databases, and cloud platforms (AWS/Azure) at a data analytics scale-up.
Design, build, and productionize LLM-powered agentic AI systems for conversational interfaces, integrating with enterprise systems to deliver scalable, real-time AI solutions for global customers.
The Lead AI Engineer will design, build, and scale production-grade AI and Generative AI solutions within the insurance platform using AWS services. This hands-on role focuses on operationalizing AI models, developing RAG pipelines, and implementing MLOps practices in a regulated environment.
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