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Build and maintain full-stack applications with AI capabilities, using modern AI tools and LLMs to prototype, deploy, and secure enterprise software for mortgage-related workflows.
Build and deploy AI-powered applications (LLMs, agents, RAG) using Databricks and modern frameworks to automate workflows and enhance decision-making across SEGA’s games business.
Lead a team building cloud-native, multi-cloud data platforms for GM’s connected services, marketing, and AI products using Databricks, Spark, and real-time streaming.
Builds enterprise AI search systems using Elasticsearch, RAG, and LLMs, plus full-stack React/Node.js apps in AWS/Azure.
Build and optimize the core in-memory database engine (C/C++) powering AWS ElastiCache and MemoryDB, extending Valkey with durability, replication, and advanced search for sub-millisecond latency at cloud scale.
Builds and optimizes high-performance vector search systems for Amazon OpenSearch, focusing on ANN algorithms, GPU acceleration, and billion-scale datasets.
Lead enterprise AI engineering at a large utility: design scalable agentic and generative AI systems in Python/AWS Bedrock, set standards, and guide production deployment for safety, reliability, and operational efficiency.
Lead the design and deployment of AI-driven cybersecurity platforms on Google Cloud, building threat detection, automation, and AI-enabled resilience for a global fintech leader.
Build and lead Python-based AI solutions, cloud deployments, and Agile delivery for enterprise clients, guiding cross-functional teams and translating business needs into scalable, secure ML systems.
Build and lead Python-based generative AI solutions using LLMs, cloud platforms, and ML pipelines while guiding cross-functional teams and translating business needs into scalable AI systems.
Build and deploy LLM-powered applications for federal systems using RAG, vector databases, and AWS Bedrock/Python, focusing on scalable, secure GenAI solutions.
Build and deploy AI-powered tools like custom GPTs and copilots using enterprise LLM platforms and RAG pipelines to automate workflows in healthcare technology.
Build and maintain shared AI services and tooling that engineering teams use to integrate AI into products, focusing on reliability, security, and cost-efficient cloud deployments.
Qdrant is an open-source vector search engine powering the next generation of AI applications, from semantic search and retrieval-augmented generation (RAG) to AI agents and real-time recommendations. Trusted by global…
Builds and deploys production-grade GenAI systems: RAG pipelines, LLM integrations, and backend services in Python (FastAPI/Flask) on AWS.
Build ML-driven data-mining tools to find the most valuable moments in petabytes of autonomous-driving data, turning them into searchable datasets for ML engineers.
About Ascend Backed by private equity from people-focused Alpine Investors, Ascend is building a dynamic platform for regional accounting firms that enables them to stay independent while accessing the resources of a…
Designs and implements enterprise GenAI search systems using Azure AI Search and Microsoft 365 Copilot, focusing on RAG, vector/semantic search, and secure retrieval across organizational data.
Chunking and ingestion architecture with deterministic, validation-gated pipelines handling diverse enterprise document types with indexing and contextual enrichment Retrieval pipeline with vector search, BM25 hybrid…
Build and scale cloud infrastructure for an enterprise AI startup, optimizing databases and deploying multi-region systems to support agentic AI workloads.
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