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NTT DATA is a trusted global innovator of business and technology services, helping clients innovate, optimize, and transform for success. We strive to hire exceptional, innovative, and passionate individuals who want…
Build and evaluate post-training pipelines for LLMs that generate clinically sound behavioral health documentation, ensuring accuracy and compliance in a high-stakes healthcare setting.
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.
The AI Solutions Architect will lead the design and implementation of end-to-end AI/ML and RAG systems for banking clients, ensuring compliance with regulatory standards. The role involves defining technical architecture across Microsoft Azure and open-source stacks while mentoring a team and establishing engineering guardrails.
Senior Software Engineer building the core software stack for an on-prem AI appliance — including containerized inference engines, CLIs, GUIs, APIs, system orchestration, and LLM/vision model integration — using Linux, containerization, and GPU/ML tooling.
Data Engineer designing and industrializing robust data pipelines and modern cloud data platforms (lakehouse, Snowflake, BigQuery, Databricks) to feed AI/agent systems and BI dashboards, using SQL, Python, Spark, Airflow, dbt, and vector databases.
The Data Engineer at Keyrus designs and industrializes robust data pipelines and modern data platforms to support AI systems and analytics. The role involves preparing data for generative AI, ensuring data quality, and collaborating with AI engineers to build scalable, production-ready data solutions.
About Mistral Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems—across high-stakes industries like…
Mistral seeks a Software Engineer, Network Automation Interessato/a a questo ruolo? Può trovare tutte le informazioni pertinenti nella descrizione qui sotto. - Data Center Fabrics to design and build systems for…
The Senior Sales Engineer will drive technical evaluations and customer success for Checkly's monitoring platform, working closely with engineering teams to solve reliability challenges. The role requires deep technical expertise in JavaScript/Node.js and experience in dev-tools SaaS to manage the full pre- and post-sales technical lifecycle.
Design and implement LLM-based architectures (Agentic AI Factory, RAG, agent runtimes) for public-sector NLP systems at a Polish government research institute.
Lead the design and delivery of production-grade chatbot and agentic AI systems at Snoonu (Qatar's super app), using AWS Lex, Bedrock, Claude, and agentic frameworks to automate customer support, order verification, and logistics workflows.
The Generative AI Engineer will design, develop, and deploy scalable AI solutions using LLMs and transformer architectures. The role involves orchestrating model workflows, integrating GenAI into enterprise systems, and collaborating with MLOps teams on cloud platforms.
Builds and scales GenAI full-stack applications (RAG, intelligent agents) for clients, bridging backend (microservices) and frontend (React) while managing cloud infrastructure, CI/CD, and LLM monitoring.
Builds and maintains scalable data pipelines, ETL processes, and analytics infrastructure to support AI model training, feature stores, and business decision-making using cloud platforms and data warehousing tools.
Designs and optimizes scalable data pipelines to enable AI models and business decisions, collaborating with ML/data science teams on data quality, feature stores, and secure deployments.
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