Top Tech Jobs & Startup Jobs

Picks, stages, labels, verifies, and loads customer orders for shipment. Operates forklifts and pallet jacks, inspects products, updates SAP and inventory records, performs cycle counts, reports discrepancies, maintains organized work areas, and follows warehouse safety procedures. Coordinates with customer service, production, and packaging teams to support accurate and timely fulfillment.

Drives revenue and profit growth across assigned institutional accounts through consultative technical sales. Builds customer relationships, executes sales plans, recommends continuous improvement solutions, provides technical support, analyzes systems and data, resolves operational challenges, and promotes water treatment technologies. Supports the Richmond/Charlottesville, Virginia territory with less than 25% overnight travel.

Drives revenue and profit growth for water treatment programs and services across Northern Virginia and Washington, D.C. Builds relationships with institutional customers, develops sales plans, converts competitive accounts, recommends operational improvements, provides technical support, analyzes systems and data, and sells water technology innovations. The role requires less than 25% overnight travel, customer-facing problem solving, and collaboration with technical service teams.

Manage strategic pulp and paper customer accounts while driving revenue and profit growth through consultative technical sales. Develop sales strategies, expand existing accounts, convert competitive accounts, build executive relationships, analyze customer systems and data, recommend improvements, and stabilize at-risk business. The role also includes coaching district representatives, leading portions of district meetings, and completing field and headquarters training.

Designs, builds, and deploys LLM-powered applications, agentic workflows, embedding pipelines, and retrieval systems. Integrates vector stores and enterprise data platforms including Databricks and Snowflake. Delivers cloud-native AI components on Azure using containers and CI/CD, while monitoring production model behavior and optimizing performance and cost.

Design and deliver scalable Microsoft Fabric data and AI solutions, including Lakehouse architectures, ingestion pipelines, medallion layers, semantic models, DAX measures, AI Agents, Data Agents, ontologies, and RAG experiences. Develop production transformations with Python, PySpark, and SQL; integrate AI capabilities into applications and APIs; and collaborate across technical and business teams. The role also emphasizes governance, testing, documentation, DevOps automation, security, and responsible AI practices.

Develop and improve hydrogen peroxide vapor decontamination products through new product development, mechanical and process testing, approval test planning, test rig development, reporting, and customer issue investigation. Provide production support by resolving quality and process problems, analyze test results, contribute to DFMEA activities, and collaborate with a multidisciplinary R&D team.

Design, build, and deploy LLM-powered applications, agentic workflows, embedding pipelines, and retrieval systems. Integrate vector stores and cloud platforms, using containerized CI/CD practices for scalable delivery. Monitor production model behavior and optimize performance and cost. The role requires Python development, LLM API integration, Databricks, Snowflake, Azure, agent orchestration, embeddings, vector databases, and API integration experience.

Administer and govern enterprise Microsoft Fabric and Power BI platforms, including capacities, workspaces, gateways, access controls, security, monitoring, and performance optimization. Automate platform operations and deployments with Python, PowerShell, REST APIs, Git, and CI/CD. Lead incident response, troubleshooting, documentation, and continuous improvement while enabling governed AI Agents, Data Agents, semantic models, and Copilot analytics capabilities.

Design, build, and deploy intelligent applications using LLMs, agent orchestration, embeddings, and retrieval systems. Develop scalable AI workflows for summarization, Q&A, decision support, and task automation. Integrate vector stores, Databricks, Snowflake, and Azure cloud services, using containerized CI/CD practices. Monitor production model behavior while optimizing performance and costs.