Senior AI Engineer
Design, build, and operate production-grade, cloud-native AI applications across the full stack. Own AI-powered features end-to-end, from initial scoping and design to deployment, monitoring, and optimization.
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Design, build, and operate production-grade, cloud-native AI applications across the full stack. Own AI-powered features end-to-end, from initial scoping and design to deployment, monitoring, and optimization.
Design, develop, and deploy advanced AI and Generative AI solutions to improve healthcare outcomes and operational efficiency. This includes managing end-to-end deployment, data engineering, and ensuring compliance with HIPAA and industry standards.
Design, build, and operate production AI systems, including agentic workflow infrastructure and LLM-powered features for financial clients. You will also develop internal tools and frameworks to improve developer productivity and AI capabilities.
Lead the technical direction and architecture of AI-native systems, including RAG pipelines and agentic workflows. Act as a player-coach by writing production code while mentoring engineers and defining engineering quality standards.
Build the product layer for a proactive AI assistant, focusing on production-grade agent workflows and reliable multi-step reasoning. Design real-time AI interactions and implement robust fallback mechanisms to ensure system reliability and observability.
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The role involves building and shipping end-to-end AI features, focusing on turning raw model outputs into reliable product behaviors. Responsibilities include designing agent workflows, optimizing for latency and cost, and debugging the full stack from model to UX.
Design and operate the inference and orchestration layer for a proactive AI smart assistant. Focus on building stable, low-latency APIs and managing production concerns like monitoring, logging, and incident response.
Review and refine AI-generated outputs regarding software engineering and system architecture. Develop real-world case studies and provide structured feedback to improve AI-driven technical recommendations.
Design and implement an autonomous learning system and RL loop to improve trading agent strategies based on live outcomes. Own the model and inference infrastructure, including the build-vs-buy decisions for model hosting and optimization.
Design and manage robust data pipelines and develop novel ML models for Large Quantitative Models and agentic frameworks. Collaborate with cross-functional teams to translate business objectives into actionable ML development and production roadmaps.
Lead the design and delivery of AI-powered product features, focusing on LLM-based systems for real-time coaching and recommendations. Architect scalable, low-latency AI systems and establish best practices for evaluation and monitoring across the engineering organization.
Design and implement AI-native systems and agentic workflows to solve product and operational problems in healthcare. Collaborate with cross-functional teams to integrate LLMs into production while ensuring reliability, security, and scalability.
Architect and scale a robust AI platform and reasoning layer to support a complex tax-focused AI agent. Build a multi-layered evaluation platform and integrate AI features into the core fintech backend using TypeScript.
Architect and build AI infrastructure and agentic workflows to transform software development and operational processes. This includes creating a centralized context layer and automating the full development lifecycle from code generation to incident triage.
Own and evolve the AI systems for ClickUp's voice platform, focusing on real-time streaming transcription and voice-to-action pipelines. Design and optimize speech-to-text pipelines and integrate LLM-powered post-processing for improved accuracy and formatting.
Develop and deploy machine learning models and build backend systems using Python. Integrate AI solutions with full-stack applications and develop REST APIs.
Develop and optimize a large neural network-based tabular model, focusing on performance bottlenecks and memory efficiency. Rewrite critical Python components in Rust or C++ to improve latency and throughput across ML pipelines.
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Design and deploy production-grade AI agents and agentic workflows using LLMs and retrieval systems. Integrate these solutions into enterprise applications and optimize performance through structured testing and observability.
Design and implement scalable architectures for AI-powered applications, integrating LLM APIs and vector databases. Collaborate with product teams to translate requirements into technical solutions and validate AI model output quality.
Serve as the Technical Lead and Authority for AI initiatives, owning the technical direction and execution of GenAI solutions. You will design AI agents and autonomous frameworks while ensuring system reliability, maintainability, and high code quality.
Design and implement agent-driven pipelines and enterprise semantic ontologies to automate the generation of business-ready star schemas. Build Python-based microservices and LLM-powered services for semantic alignment and metadata generation on Microsoft Fabric.
Design and implement AI solutions using RAG and autonomous agent networks to solve complex business problems. Manage data ingestion pipelines, serverless architectures, and MLOps practices to automate the deployment of AI models.
Design and scale asynchronous APIs and multi-agent AI workflows using Python and Semantic Kernel. Implement RAG systems and integrate various LLM providers while maintaining robust CI/CD pipelines and observability.
Build and scale an AI-powered financial research platform by designing data pipelines for SEC filings and market data. Develop backend APIs and semantic search systems to power AI workflows and internal analytics dashboards.
Implement and deploy end-to-end enterprise AI solutions and RAG pipelines on the Azure stack. Establish AI guardrails, observability, and CI/CD pipelines to ensure secure and compliant production-ready AI services.
The role serves as the technical owner of product quality, building and automating the entire quality function for AI, software, and automation solutions. Responsibilities include developing QMS frameworks, validating AI agent behavior, and creating internal automation tools to monitor AI output and drift.
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Architect and deploy production-grade RAG pipelines and agentic workflows to process complex multimodal data for government and military clients. Balance high-level R&D and experimentation with the delivery of scalable, production-ready software on AWS.
Design and deploy end-to-end AI-powered products and autonomous agents to automate mortgage lending workflows. Build document intelligence pipelines and AI-driven borrower experiences to compress complex tasks into minutes.
Lead the research and application of modern AI methods to build agentic chat capabilities and product features. Architect scalable, production-grade AI infrastructure and collaborate with cross-functional teams to define the AI roadmap.
Lead the design and implementation of production AI systems to solve business problems for partners. Drive the organization's AI strategy through technical leadership, innovation, and mentorship of engineering teams.
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