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Architect and build high-throughput, low-latency FastAPI microservices for a cloud-native backend, using async Python, PostgreSQL, Redis, and Kubernetes.
Senior Data Engineer builds and scales Python-based ELT/ETL pipelines, data warehouses, and DataOps practices for high-growth client products using tools like dbt, Airflow, and Snowflake.
Build and scale the backend platform that powers AI-driven travel experiences, integrating LLMs, vector search, and agentic systems for millions of users.
Design enterprise AI solutions using generative agents, RAG, and ITSM/AIOps workflows to automate IT support, diagnostics, and operations with human-in-the-loop oversight.
Build and maintain cloud-native geospatial platforms that ingest, process, and serve Earth Observation data via APIs and microservices using Python, Kubernetes, and open standards like STAC/OGC.
Build and operate cloud-native geospatial platforms that power AI-driven Earth Observation services, integrating RAG, vector databases, and Kubernetes-based infrastructure.
Build and maintain the internal AI platform that powers AI features across MeridianLink’s products, including model serving, RAG infrastructure, prompt management, and evaluation pipelines.
Build and maintain the back-end infrastructure for an AI-driven knowledge platform, focusing on knowledge graphs, vector databases, and data pipelines using Python/Java, SPARQL, and REST APIs.
Build and maintain the back-end infrastructure for an AI-driven knowledge platform, including knowledge graphs, vector stores, and APIs using Python/Java, SPARQL, and cloud services.
Builds and deploys autonomous AI agents, multi-agent systems, and generative AI solutions using frameworks like LangChain, AutoGen, and Azure OpenAI to automate enterprise workflows, enhance decision-making, and deliver scalable AI-driven business value.
Lead a team building and scaling enterprise data platforms that power Citi’s AI, machine learning, and analytics, designing high-performance pipelines and feature stores for real-time and batch processing.
Deploy and optimize large language models (LLMs) in production, quantize models, fine-tune with SFT/LoRA/RLHF, and build RAG/agentic pipelines for high-load systems.
Build and deploy production-scale AI systems at Adobe, focusing on LLMs, RAG pipelines, and Agentic workflows using Python, cloud platforms, and GenAI libraries.
Build and operate cloud-native geospatial platforms that power AI-driven Earth Observation services, integrating RAG, vector databases, and Kubernetes-based workflows.
Build and maintain cloud-native geospatial platforms that ingest, process, and serve Earth Observation data using open standards like STAC/OGC APIs and tools such as GDAL, Xarray, and Kubernetes.
Builds and ships production-grade AI features for a retail merchandising platform using LLMs, RAG, and agentic AI systems in Python.
Build and deploy secure AI-powered web apps in a restricted DoD environment using Python, React, and RAG pipelines with LLMs like Llama 3.
Build and deploy enterprise-grade AI applications using LLMs, RAG, AI Agents, and vector search; design scalable backend services and orchestration pipelines for agentic workflows.
Build and maintain the internal AI platform that powers MeridianLink’s products, including model serving, RAG infrastructure, prompt management, and evaluation pipelines.
Lead AI platform engineer designing and deploying enterprise Agentic AI solutions for Fortune 500 customers using LLMs, RAG pipelines, and Java/Python services.
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