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Lead Data and AI Solution Engineer responsible for architecting and delivering innovative data and AI solutions, including GenAI, Agentic AI, and cloud-based architectures (AWS/Azure/GCP), while driving AI governance and mentoring teams.
Builds production-grade GenAI and agentic AI systems on GCP, including RAG pipelines, embeddings, and ADK-based agents, while designing reliable data pipelines for travel retailing.
Designs and optimizes GPU-based infrastructure for GenAI/LLM workloads, focusing on distributed training, performance tuning, and scalable deployments across cloud and on-prem environments.
Develops and validates AI/Generative AI pipelines in Databricks, ensuring data quality, governance, and performance for risk-modeling solutions in a fintech environment.
Design and validate data pipelines and GenAI solutions on Databricks using Python, Spark, and SQL. Manage model risk and data governance, implementing MLOps/LLMOps practices in a hybrid environment.
Role Overview｜岗位概述 We are looking for a Senior AI Systems Engineer with deep experience in multi-model systems, model routing, AI evaluation, and production AI infrastructure. The role will focus on designing systems…
Own and scale the AI-SDLC platform’s deployment, CI/CD, and observability to productionise AI workflows for multiple teams, ensuring enterprise-grade reliability, security, and cost control.
The AI Engineer will design, build, and deploy enterprise-grade Generative AI applications, including RAG solutions and agentic workflows, to support digital transformation. The role involves hands-on development with LLMs, Python, and cloud AI platforms while ensuring system performance, safety, and governance.
The Junior AI Software Engineer will develop Python backend services and APIs to support machine learning and Generative AI applications. The role involves building and integrating LLMs, RAG workflows, and AI agents while ensuring model performance and safety through MLOps practices.
Senior Data Scientist building and scaling connected agentic AI systems (multi-agent LLM workflows, RAG pipelines) in Python and SQL at AB InBev's Bangalore office, partnering with business stakeholders to deliver production-ready ML solutions.
Build and maintain Python backend services and APIs for ML and Generative AI applications—including LLM, RAG, and AI agent workflows—deployed into cloud/GCC environments with a focus on Responsible AI controls and production support.
Lead the engineering efforts of an 'Agentic Factory' team, designing and deploying production-grade GenAI/ML solutions, multi-agent workflows, and MLOps pipelines on GCP/Vertex AI with hands-on Python development and team mentorship.
This role involves designing and implementing automation frameworks and LLMOps solutions to streamline client operations within a managed services environment. The engineer will focus on CI/CD, infrastructure as code, and deploying agentic AI applications to production.
Lead and scale Acosta Group's enterprise AI architecture and platform engineering, defining reference architectures and building reusable AI platform services across GenAI, agentic AI, and RAG using Azure, Azure AI Foundry, Kubernetes, and MLOps/LLMOps.
Build and deploy generative AI features and full-stack apps to modernize Barclays’ platform, using Python/Java, cloud-native tools, and enterprise-grade security.
Builds and deploys AI-driven systems (RAG, agents) for Thomson Reuters’ investigative platform CLEAR, integrating AI into full-stack web apps, APIs, and enterprise workflows for legal/tax/compliance domains.
Builds AI-powered automation solutions for internal business processes (Finance, HR, Logistics) by designing agentic systems, integrating LLMs/RAG, and deploying scalable production-grade models while ensuring security, governance, and observability.
The Principal AI Engineer will lead the architecture and development of AI agents and machine learning systems for Venture Global LNG. This role involves hands-on prototyping, setting engineering standards for cloud and on-premises environments, and mentoring senior engineers to build scalable, safety-first AI solutions.
The Senior Machine Learning Engineer will manage the end-to-end lifecycle of ML and LLM-based solutions, focusing on model optimization, deployment, and MLOps infrastructure. The role involves developing agent systems and collaborating with cross-functional teams to integrate scalable machine learning models into production.
Lead hands-on design and delivery of AI solutions, agents, copilots, and RAG workflows using Azure AI and Databricks, while establishing reusable engineering patterns and guiding teams from prototypes to production.
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