Python AI Jobs

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Assist customer engagements from start to end by running discovery calls and demos, building and maintaining world class agents, participating in customer calls, and serving as the primary point of contact in a fast-paced environment. Own the full agent development life cycle including building and prototyping quickly, setting up CI/CD, monitoring live usage, iterating to targets, debugging live issues, communicating with customers, and documenting best practices to accelerate future projects. Close the feedback loop with product and platform teams by capturing unmet needs, prototyping new features, contributing directly to the codebase, and collaborating with core teams to strengthen the platform for all customers.

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Participate in on-call rotation (Pagerduty) to respond to production incidents. Build and run infrastructure with Ansible, Terraform, and Kubernetes to enable scaling to a large number of concurrent users. Build monitoring systems to ensure the highest quality service for customers. Design and implement operational processes such as deployments and upgrades. Debug production issues across all services and levels of the stack. Identify improvements for the product architecture from the perspectives of reliability, performance, and availability. Plan the growth of Together AI's infrastructure.

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As an AI Infrastructure Engineer at Together, the responsibilities include participating in on-call rotation to respond to production incidents, building and running infrastructure using Ansible, Terraform, and Kubernetes to support scaling to a large number of concurrent users, building monitoring systems to ensure high-quality service, designing and implementing operational processes such as deployments and upgrades, debugging production issues across all services and stack levels, identifying improvements for product architecture in terms of reliability, performance, and availability, and planning the growth of Together AI's infrastructure.

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As an AI Infrastructure Engineer at Together AI, the responsibilities include participating in on-call rotation (Pagerduty) to respond to production incidents; building and running infrastructure with Ansible, Terraform, and Kubernetes to enable scaling for a massive number of concurrent users; building monitoring systems to ensure the highest quality service for customers; designing and implementing operational processes such as deployments and upgrades; debugging production issues across all services and levels of the stack; identifying improvements for the product architecture from reliability, performance, and availability perspectives; and planning the growth of Together AI's infrastructure.

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Build and deploy AI Agents including prompt design, workflow configuration, integrations, telephony setup, and evaluation frameworks. Act as the primary technical partner for customers by leading demos, communicating progress, gathering feedback, and guiding solutions from concept to production. Configure and connect systems using APIs, handling authentication, data mapping, error handling, and integrations with CRMs, knowledge bases, and other enterprise tools. Set up telephony systems including SIP/CCaaS/PSTN routing, pass metadata, configure fallbacks, and troubleshoot call quality. Write and refine prompts for LLM-driven agents, monitor performance, and ensure agents meet automation and containment targets. Translate customer requirements into actionable solutions and work consultatively to unblock challenges in security, connectivity, or knowledge ingestion. Collaborate with product and engineering teams to address platform gaps and resolve technical issues, independently driving leading client implementations.

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Design and deliver production-grade AI agents that are highly performant, reliable, and intuitive, driving revenue directly to Sierra's growth across industries like finance, healthcare, and commerce. Take complete ownership and autonomy in the Agent Development Life Cycle from initial pilot through deployment and continuous iteration, including building, tuning, and evolving AI agents in production environments while defining best practices. Work directly with leaders of large enterprises and cutting-edge startups to understand their business challenges and develop AI agents that transform their operations at scale. Guide the evolution of Sierra's core platform through customer interaction, surfacing unmet needs, prototyping new tools and features, and collaborating with research, product, and platform teams to shape AI agent development and Sierra's products.

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As an Agentic Product Analyst at Netomi, you will be responsible for designing, architecting, and deploying large-scale Agentic AI solutions for enterprise customers. This includes leading discovery sessions with customers to understand business processes, identifying automation opportunities, and designing agentic orchestration strategies using Netomi's AI platform. You will build detailed solution blueprints covering workflows, data exchanges, escalation logic, analytics, and agent lifecycle design. Defining end-to-end Agentic AI architectures and working with customer technical teams to map integration dependencies are also key tasks. You will own the creation of integration design documents, support Integration Engineers during implementation, and ensure agent workflows comply with enterprise standards. Collaboration with Product & Engineering to translate requirements into features, serving as product owner during deployment, validating solution behavior with QA, conducting user-experience reviews, training customer teams, and ensuring projects deliver on time with measurable impact also fall under your responsibilities. Additionally, you are expected to act as a trusted advisor to customer stakeholders, present architectural recommendations, drive continuous improvement, and maintain deep expertise in agentic AI, LLMs, workflow orchestration, and enterprise systems.

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Design and build scalable backend systems powering AI Agents that operate in real-time enterprise environments. Develop agent orchestration frameworks involving multi-step reasoning, tool usage, and decisioning workflows. Build systems for agent memory, context management, and state persistence across interactions. Architect low-latency inference pipelines integrating Large Language Models, Small Language Models, and external tools/services. Implement evaluation frameworks to measure agent performance, accuracy, and reliability. Enable continuous improvement loops for AI agents in production including feedback, retraining, and deployment. Design and manage event-driven, asynchronous workflows for complex agent tasks. Optimize systems for high throughput, low latency, and cost-efficient inference at scale. Build and maintain robust APIs and service layers (REST/gRPC) for agent capabilities. Partner closely with Applied AI/ML teams to productionize models and agent behaviors. Collaborate with Product and Solutions teams to translate real customer workflows into agentic systems. Drive best practices in observability, monitoring, safety, and guardrails for AI systems. Contribute to architecture decisions for scaling multi-tenant, enterprise-grade AI platforms.

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Build end-to-end product experiences that span frontend applications, backend services, agent workflows, cloud infrastructure, and developer tooling. Design AI-powered workflows that generalize across a wide variety of software engineering teams, languages, codebases, and development practices. Discover and implement novel ways to apply AI to eliminate friction throughout the software development lifecycle. Partner closely with product, design, and research to understand developer needs and rapidly translate insights into shipped product improvements. Work directly with users—including developers at OpenAI, open-source contributors, startups, and large enterprises—to understand pain points and validate solutions. Improve the reliability, observability, scalability, and performance of the systems and workflows you build.

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Build core primitives end to end including entity ownership, audit, authorization, and orchestration, ensuring the right actions are the default and incorrect actions are difficult. Own the domain model by turning Fluidstack's concepts of power, datacenters, and chips into composable entities that remain durable over time. Define interactions with external systems by interfacing with vendor systems and ingesting domain-specific formats such as KMZ, BIM, Revit, and vendor documents. Enable AI agents to be first-class operators of Fluidstack's systems by providing tools, guardrails, and audit trails to allow safe and effective operation beyond mere advice.