Field Engineering Manager, Public Sector
As a Production AI Ops Lead, you will design and develop the production lifecycle of full-stack AI applications, support end-to-end system reliability, real-time inference observability, sovereign data orchestration, high-security software integration, and resilient cloud infrastructure for international government partners. Responsibilities include owning the production outcome with full accountability for long-term performance and reliability of AI use cases across international government agencies, ensuring full-stack integrity by overseeing all platform components from APIs to UI for a production-ready environment, building automated systems to monitor model performance and data drift across dispersed environments, managing the technical lifecycle within diverse regulatory frameworks, leading incident response in mission-critical environments with rapid resolution and prevention guardrails, translating technical performance metrics into clear insights for senior government officials, and partnering with engineering and ML teams to influence the technical architecture and decisions for future AI use cases.
Aerodynamics Methodology and Software Engineer
Refactor research scripts and specialist tools into modular, high-performance, and maintainable Python/C++ libraries, implementing robust unit-testing and documentation standards, and ensuring the team follows code development structure. Architect agentic workflows and custom MCP servers to connect LLMs with internal CFD solvers and databases, codifying engineering knowledge into structured files to enable AI-driven code refactoring, automated simulation setup, and intelligent data analysis. Develop APIs and automated workflows to integrate tools like OpenVSP, XFoil, and OpenFOAM into seamless optimization loops. Manage and optimize Linux-based HPC clusters and/or Cloud computing infrastructure. Design the data architecture for storing and retrieving aerodynamic results to provide vehicle performance data as a single source of truth for GNC and flight physics teams.
Software/AI Engineer (New Grad)
Develop, test, and deploy production-level code across backend and AI systems. Collaborate with AI researchers to integrate and optimize large language models for insurance workflows. Build data processing and evaluation pipelines for unstructured document inputs such as PDFs, emails, and images. Contribute to core infrastructure including APIs and orchestration logic powering the AI Workspace for Insurance. Work cross-functionally with product and customer teams to identify and solve real business problems using AI. Participate in design reviews, code reviews, and rapid iteration cycles.
Software Engineer, AI Platform
Design and build abstractions and platform-level systems that improve all of Harvey’s agentic products; own infrastructure for model integration, routing, and evaluation that helps Harvey choose and deploy the right foundation model for any given context; build evaluation frameworks and tooling that let every team across Harvey iterate on AI quality effectively; partner closely with product engineering teams, PMs, and design to launch cutting-edge AI products; evaluate, prototype, and integrate the latest advancements in AI and agentic systems as they emerge.
Forward Deployed Engineer, Agentic Platform (Public Sector)
Build and ship features for North, Cohere's AI workspace platform; develop autonomous agents that interact with sensitive enterprise data; experiment rapidly and with high quality to engage customers and deliver solutions that exceed expectations; work across the entire product lifecycle from conceptualization to production; lead end-to-end deployment of North in private cloud and on-premises environments, including planning, configuration, testing, and rollout.
Software Engineer, AI Platform
Design and build abstractions and platform-level systems that improve all of Harvey’s agentic products. Own infrastructure for model integration, routing, and evaluation that helps Harvey choose and deploy the right foundation model for any given context. Build evaluation frameworks and tooling that let every team across Harvey iterate on AI quality effectively. Partner closely with product engineering teams, PMs, and design to launch cutting-edge AI products. Evaluate, prototype, and integrate the latest advancements in AI and agentic systems as they emerge.
Senior Brand Events Manager
Own the observability and lifecycle management of AI features across the organization. Build tools and infrastructure to enable teams to develop, monitor, and optimize LLM-powered features. Design and implement closed-loop evaluation pipelines that automatically validate prompt changes. Develop comprehensive metrics and dashboards to track LLM usage: cost per feature, token patterns, and latency. Create systems that tie user feedback to specific prompts and LLM calls. Establish best practices and processes for the full lifecycle of prompts: development, testing, deployment, and monitoring. Collaborate with engineering teams across the organization to ensure they have the tools and visibility needed to build high-quality AI features.
Principal AI Ops Architect, IPS
As a Production AI Ops Lead, you will design and develop the production lifecycle of full-stack AI applications, support end-to-end system reliability, real-time inference observability, sovereign data orchestration, high-security software integration, and resilient cloud infrastructure for international government partners. You will take full accountability for the long-term performance and reliability of AI use cases deployed across international government agencies. You will oversee the end-to-end health of the platform ensuring seamless integration between the AI core and all full-stack components, from APIs to UI, to maintain a responsive and production-ready environment. Build automated systems to monitor model performance and data drift across geographically dispersed environments ensuring reliability. Manage the technical lifecycle within diverse regulatory frameworks. Lead the response for production issues in mission-critical environments, ensuring rapid resolution and building guardrails to prevent recurrence. Translate deep technical performance metrics into clear insights for senior international government officials and partner with Engineering and ML teams to ensure field lessons influence the technical architecture and future use cases.
Senior Product Designer, Mobile
Own the observability and lifecycle management of AI features across the organization. Build tools and infrastructure to enable teams to develop, monitor, and optimize LLM-powered features. Design and implement closed-loop evaluation pipelines that automatically validate prompt changes. Develop comprehensive metrics and dashboards to track LLM usage, including cost per feature, token patterns, and latency. Create systems that tie user feedback to specific prompts and LLM calls. Establish best practices and processes for the full lifecycle of prompts, including development, testing, deployment, and monitoring. Collaborate with engineering teams across the organization to ensure they have the tools and visibility needed to build high-quality AI features.
Lazo - Head of Engineering
The Head of Engineering at Lazo is responsible for owning the technology strategy and roadmap aligned with business and product OKRs, defining the reference architecture for agentic systems, establishing security and compliance baselines including SOC2-readiness, and presenting trade-offs, risks, and progress in leadership reviews. They are also tasked with shipping backend services in Python/TypeScript, driving high-impact PRs and code reviews, orchestrating agents and toolchains, integrating external APIs and databases, and building robust pipelines. The role includes end-to-end DevOps responsibilities such as AWS/GCP management, containerization, IaC, CI/CD, observability, and on-call design, as well as reducing technical debt, improving latency and throughput, and managing infrastructure costs. The individual defines SLOs and error budgets, reduces MTTR and change-fail rates, implements data access policies and secure data flows for AI features, drives post-mortems and preventive engineering practices, hires and mentors engineers, sets performance scorecards with integrated operating systems, fosters a culture of thoughtful trade-offs and fast feedback, partners with Product and AI teams to turn customer problems into scalable solutions, collaborates with Ops, Growth, and Customer teams for reliability and launch readiness, and manages vendors and evaluates build-vs-buy decisions.
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