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Design and build scalable data ingestion and streaming pipelines on Azure/AWS that feed AI and analytics systems, working with structured enterprise data and IoT/edge device data from manufacturing environments.
Data Engineer building and maintaining scalable ETL data pipelines for AI/ML model training, using Python, SQL, and tools like Airflow in an onsite role in Pune.
Lead architect for Staples' enterprise observability platforms (APM, infrastructure monitoring, session replay, log analytics), designing and standardizing scalable monitoring solutions across application and infrastructure layers.
Builds and deploys generative AI and agentic systems using LLMs, LangChain/LangGraph, and Python to automate workflows and support healthcare applications.
Senior Backend Engineer building and scaling web scraping infrastructure and LLM-powered data normalization pipelines for a healthcare data company, using Python, SQL (Postgres), and AWS.
Senior Data Engineer builds and maintains scalable data pipelines and platforms using Databricks, Python, SQL, and cloud tools to support analytics and reporting in Singapore.
Designs and builds high-throughput cloud data pipelines and low-latency APIs to ingest and serve real-time telemetry data at scale.
The Forward Deployed Engineer works with strategic clients to design, build, and scale AI integrations and workflows using Python, Go, and cloud infrastructure. This role bridges the gap between client needs and the CloudFactory platform, focusing on moving AI systems from proof-of-concept to production.
Architect and lead the design of enterprise observability platforms—APM, infrastructure monitoring, session replay, and log analytics—partnering with Engineering, SRE, and Infrastructure teams to drive system reliability and performance at Staples.
Staff Engineer leading architecture, design, and hands-on development of enterprise AI/GenAI solutions—LLMs, RAG, and agentic workflows—using Python, FastAPI, LangChain/LangGraph, and Azure OpenAI at a global life-sciences company.
Hands-on technical leadership role designing, developing, and deploying advanced AI/ML solutions—including LLM applications, RAG, and agentic workflows—for scientific products at a major life sciences company. Core stack: Python, PyTorch, LangChain, LangGraph, Azure OpenAI, and vector search.
Builds production-grade AI/Generative AI solutions (LLMs, RAG, agentic workflows) for scientific/healthcare systems, integrating models into backend services and cloud platforms.
Develops and deploys generative AI and deep learning solutions for Citi’s Treasury & Trade Services, extracting insights from unstructured data (emails, call transcripts) to drive client experience and revenue growth. Core focus: end-to-end AI product delivery, LLM fine-tuning, RAG systems, and MLOps for production-grade GenAI.
Line of Service Assurance Industry/Sector Not Applicable Specialism Assurance Management Level Senior Manager Job Description & Summary At PwC, our people in audit and assurance focus on providing independent and…
Hands-on senior AI/ML engineer leading the design, development, and production deployment of LLM, RAG, and agentic AI solutions for life sciences and healthcare applications using Python, PyTorch, LangChain, and Azure OpenAI.
Leads end-to-end AI/ML development for scientific workflows, deploying LLMs, RAG, and agentic systems in healthcare/genomics to improve customer outcomes and innovation.
The Lead Software Engineer will design and develop data platform solutions for asset management compliance and risk, focusing on cloud migration, data ingestion pipelines, and data quality. The role requires expertise in Oracle PL/SQL, Python, and AWS to build scalable investment tools.
Job Description Adobe Cloud Technology Group is building a collaboration platform for creative, marketing, and document workflows. We're enabling data-driven features at scale and pushing the future of SaaS…
This role serves as a dual Product Owner and Project Manager for data engineering initiatives, managing backlogs and project execution within an agile environment. The position focuses on building data infrastructure and intelligence layers for connected assets using tools like Azure DevOps.
Design and deploy AI-native software products that transform geospatial data into actionable intelligence, using LLMs, multi-agent orchestration frameworks (LangChain, LangGraph, Google ADK), and cloud infrastructure on GCP/AWS.
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