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The Gen AI Engineer designs and implements retrieval-augmented generation (RAG) pipelines for banking clients using the Azure AI stack and Python-based frameworks. The role focuses on building secure, traceable, and accurate AI systems, including prompt orchestration, guardrails, and vector database management.
This GenAI Engineer role focuses on designing and implementing RAG pipelines and agentic AI solutions for banking clients using the Azure AI stack and Python-based frameworks. The position requires building secure, compliant, and traceable AI systems with a strong emphasis on guardrails, vector databases, and model evaluation.
Builds enterprise Python applications using FastAPI and Kubernetes for global clients across finance, healthcare, and mobility.
Senior Python developer building enterprise-scale applications with Python, FastAPI, Kubernetes, and CI/CD pipelines as part of a global development team.
Closure Technologies is seeking a AI/ML Engineer who will Implement and maintain Retrieval-Augmented Generation (RAG) pipelines and integrate Large Language Models (LLMs) into applications, supported by API development…
Builds and deploys AI-powered applications and the infrastructure that runs them, focusing on LLM integration, agentic systems, and automation across cloud and on-premise environments.
Leads end-to-end design and deployment of production-grade AI/ML and GenAI solutions, focusing on RAG platforms, agentic systems, and secure enterprise integrations using Python, AWS/Azure, and MLOps practices.
Build and deploy end-to-end GenAI/LLM applications (RAG, agentic workflows, evaluation pipelines) using Python, cloud platforms, Docker, and vector databases for a finance consulting firm in Pune.
The Data Scientist will design and implement scalable AI and Generative AI solutions to improve business efficiency and customer experience. The role involves building production-grade applications using Python, SQL, LLMs, RAG, and agentic AI frameworks.
Design, build, and operate scalable data infrastructure and MLOps platforms for Doodle's B2B SaaS scheduling product, using Python, SQL, cloud infrastructure, containers, and IaC.
Senior Consultant applying data science, AI, and analytics to healthcare and life sciences commercial challenges—building predictive models, supporting AI solutions, and delivering client-ready insights using Python, SQL, and ML techniques.
Leads AI/ML science and engineering for Compass, an agentic retailer assistant combining proprietary data, LLM, and web search to drive smarter buying decisions. Owns end-to-end feature development, agent quality metrics, and cross-stack architecture while shipping high-velocity product innovations.
Staff ML Engineer architecting and deploying production GenAI systems (LLM-powered features, RAG workflows, agentic apps) on a team building AI-powered identity/security infrastructure, primarily using Python.
Design and deploy production-scale GenAI systems, including RAG pipelines, agent frameworks, and evaluation workflows, to power secure AI-powered identity features.
Senior Lead AI Engineer managing technical projects and building production AI/ML systems, LLM-based agents, and pipelines for a global management consulting firm's AI practice.
ML Engineering Intern on the Knowledge Assist team building RAG, generative knowledge assist, and enterprise search solutions for contact centers using Python and deep learning frameworks.
Build and scale enterprise AI systems using Python, transformer models, and RAG pipelines; design agentic workflows and optimize ML observability in a hybrid Montreal office.
Senior Software Engineer building LLM-powered AI agents and a knowledge/reasoning platform for an early-stage HR tech startup in Toronto, using Golang, Ruby on Rails, Python, AWS, Kafka, and related infrastructure.
Build and deploy production-grade ML and GenAI systems for insurance, banking, and wealth management using Python, MLOps, and cloud-native tools.
Design, build, and scale enterprise-grade AI systems using Python, Agentic Workflows, deep learning, NLP, and Generative AI, with focus on RAG pipelines, tokenization, embeddings, and MLOps observability.
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