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Design and evolve a large-scale, event-driven microservices backend for Wix's User Voice platform, which processes millions of user feedback signals through an AI pipeline (LLMs, NLU, semantic clustering) to produce actionable product insights.
Мы строим новое AI-направление и создаем команду, которая станет центром компетенций по искусственному интеллекту и будет развивать AI-функциональность сразу в нескольких продуктах компании. Сейчас мы формируем команду…
The AI Agent Developer / DevOps Engineer will develop and maintain AI agents and cloud-native backend components using Azure OpenAI and containerized services. The role involves designing agent architectures, managing CI/CD pipelines, and ensuring system security and observability within a public sector consulting environment.
The Data & Integration Engineer will design and implement data flows, APIs, and pipelines to support GenAI initiatives. The role involves working with enterprise data platforms like Informatica and Cloudera to ensure reliable data movement and preparation for AI workflows.
This role involves building agentic AI systems to support rare disease research at the NIH, focusing on conversational workflows that capture structured data from clinical and scientific sources. The position requires extensive experience in production-grade LLM application engineering, including prompt engineering, structured output, and system evaluation.
Backend Engineer (Python) building scalable services, APIs, and LLM-powered features for an AI-first finance platform, working with Python, AWS, SQL, and REST APIs.
Senior AI Engineer designing and operationalizing ML/AI solutions—predictive models, GenAI assistants, RAG workflows, and agents—for Teradyne's IT organization, building MLOps/LLMOps pipelines and production-grade practices using Azure AI Foundry, Copilot Studio, Anthropic Claude, Vertex AI, and Snowflake.
Data Scientist supporting Medicare/Medicaid fraud, waste, and abuse detection using Python/R, machine learning, and generative AI tools on claims data for federal health programs.
Senior Data Scientist applying ML, generative AI, and agentic systems to detect and prevent Medicare/Medicaid fraud, waste, and abuse using Python, Spark, and LLM orchestration tools.
Senior AI Engineer designing, building, and operating production-grade Generative AI solutions on the Microsoft Azure AI ecosystem—from model selection and RAG architecture through deployment, MLOps, security, and governance.
Data/AI Engineer on a 4-month hybrid contract in Cork, building data solutions using LLMs—specifically RAG, embeddings, and vector databases—applied to unstructured data.
Lead Data Scientist at Mastercard building applied ML solutions (forecasting, propensity modelling, recommendations) using Python, SQL, and ML frameworks, leading technical teams from concept to production.
Build AI data solutions using LLMs, embeddings, vector databases, and RAG workflows on a 4-month hybrid contract in Cork.
Senior Data Engineer leading the design and operation of AWS-based data warehousing, ELT pipelines, QuickSight reporting, and GenAI/LLM-powered analytics capabilities for a fintech company.
Build and evolve MCP servers, graph-RAG pipelines, and agentic layers that connect a platform to multiple LLMs (Claude, Gemini) using Python, vector databases, and graph data stores.
AI Engineer building production-grade AI/ML and GenAI solutions (RAG, agents, NL interfaces) on Snowflake Cortex AI, using Python, SQL, and Microsoft Azure in a hybrid Toronto role.
Builds and evolves MCP servers, tools, and graph-RAG pipelines to expose platforms to LLMs (e.g., Claude, Gemini) using Python, RAG, and vector databases, collaborating with product, data, and QA teams in an agile model.
Full stack developer at Thales Canada building backend services, containerized deployments, and generative AI integrations (RAG pipelines, vector embeddings, MCP tools) for the Canadian Department of National Defence, primarily using Python, Docker, Kubernetes, and Helm.
Lead application development projects building secure APIs, system integrations, and AI-enabled capabilities (LLM APIs, RAG, AI agents) on cloud platforms (AWS, Azure, GCP), managing full SDLC with Agile/DevOps practices.
Design and build AWS-based data architectures—S3 data lakes, Glue ETL pipelines, Redshift/Snowflake/Databricks warehouses, and RAG-powered AI systems—across diverse client engagements, with mentoring and architecture presentation responsibilities.
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