Applied Ai Architects Manager Jobs

CSQ426R319 Senior Customer Enablement AI Programs Manager As a Senior Customer Enablement AI Programs Manager, you will own the AI strategy and roadmap for the Scaled Customer Enablement Agent Suite — bringing together GTM activation, applied AI, and enablement strategy. You will define how the agents retrieve, reason over, and surface enablement insights to global Account Teams, turning learner and account data into prescriptive, just-in-time recommendations that improve account health and grow the number of trained users across our customer base. By automating GTM processes, you will ensure Account Teams and customers receive consistent Databricks enablement planning resources. You will be responsible for driving the AI roadmap for customer enablement, compounding enablement as a strategic leverage for our customers and for Databricks. This role will report to the Global Customer Enablement Practice Senior Director. This is an exciting opportunity for a motivated and innovative entrepreneur with a passion for working across departments and across regions. The impact you will have: Own the product roadmap and architecture direction for the Scaled Customer Enablement Agent Suite — defining the data inputs, retrieval sources, and agent behaviors that generate enablement recommendations. Lead the rollout and drive field adoption of the Scaled Customer Enablement Agent Suite, delivering automated learning insights and proposals to Account Teams. Structure the enablement knowledge base for retrieval — designing content schemas, metadata, and chunking so agents return accurate, grounded outputs — and maintain the underlying customer enablement playbook. Define the signals and triggers — usage patterns, learning milestones, and account-health thresholds — that prompt the agents to surface a free-to-paid enablement opportunity to Account Teams at the right moment. Partner with technical teams to embed enablement calls-to-action and insights di

About the Role The Senior AI Evals Test Engineer is a hands-on technical role responsible for defining, developing, and executing robust evaluation frameworks and test strategies for AI agents built using the Google Agent Development Kit (ADK) or similar sort of agentic frameworks . This role bridges AI development and reliable deployment — ensuring our agents are safe, ethical, effective, and meet high-quality performance standards. A significant focus of the role is on Responsible AI and Safety Evals, including adversarial testing, red-teaming, bias detection, and toxicity measurement . The engineer will design scalable evaluation pipelines , build synthetic test environments, and work closely with AI/ML Engineers, Researchers, and policy teams to ensure agents are production-ready and compliant with regulatory and ethical standards. The role is 70% Automation and 30% Manual Testing , and sits within the Applied AI: Test Engineering team as a key technical authority. Key Responsibilities Design and build agent evaluation frameworks — create end-to-end infrastructure for evaluating LLM-based agents across correctness, latency, cost, safety, and task completion rate Build synthetic testing environments and simulation strategies to stress-test AI agents under diverse real-world conditions, with specific focus on agents built on Google ADK and related Google AI/ML services (e.g., Vertex AI, Gemini models) Build golden datasets and test harnesses — curate domain-specific evaluation sets for Rubrik's product verticals (data security, SaaS protection, cyber recovery, compliance) Establish eval-as-a-service — build and enhance centralized evaluation platform that any IT team members can self-serve to test their agents against standardized and custom benchmarks Automate regression and drift detection — implement CI/CD-integrated eval pipelines that catch regressions on every model or prompt change before production deployment Define eval taxonomies and rubrics — est

Job Requisition ID # 26WD97978 Position Overview The DFA Applied AI team at Autodesk builds intelligent, AI-powered solutions that enable users to discover, access, manage, and collaborate on design and manufacturing data through natural language and intuitive user experiences. Our work combines LLM-powered agents, scalable backend services, and reusable UI components to deliver seamless, connected workflows across Autodesk products and data sources. Responsibilities Lead the architecture, design, and delivery of full-stack AI-powered solutions, from backend agent services to React/TypeScript user experiences. Design and develop LLM-powered agents, including tool calling, orchestration, streaming responses, and multi-turn conversational workflows. Build scalable backend services, APIs, and integrations that enable secure access to design and manufacturing data. Develop reusable React/TypeScript components and intuitive user experiences for AI-powered and data access workflows. Drive technical decisions across agent architecture, LLM integration, backend services, and cloud infrastructure. Ensure production readiness through robust testing, security, performance, reliability, and observability. Mentor engineers, establish engineering best practices, and own features from design through production. Minimum Qualifications 10+ years of software engineering experience building and delivering large-scale production systems. Hands-on experience with LLM application patterns, including tool use, streaming responses, multi-turn conversations, and context/session ma

Toradex is a global company strongly focused on engineering & technology. We’re powered by a diverse & uniquely gifted workforce. We pursue the best people to propel our innovative vision of embedded computing and IoT. If you’re interested in being a driving force at an agile technology company, engineering clever computing solutions & helping other companies bring their products to life, we should talk. About the role Toradex is looking for an engineer to build and operate AI-assisted workflows that keep technical product documentation accurate and current. You will own the flow from engineering information and customer support resolutions to reviewed documentation updates. Working directly with these updates, you will establish reliable sources, structure information, and automate how changes are drafted and verified. The role combines Python development, applied AI, and embedded-systems understanding, with guidance from our engineering and DevEx teams. What you will do Own selected product-information workflows, including revision histories, errata, technical resources, and documentation updates, improving their automation and reliability as you operate them Map authoritative sources and information owners, resolve discrepancies with engineering, and preserve traceability to applicable products, hardware revisions, and software versions. Build workflows that turn source changes, sufficiently detailed tickets, and resolved support cases into proposed documentation updates. Work with documentation specialists to define reusable templates and explicit quality criteria that guide both AI generation and review. Implement checks for completeness, consistency, factual support, and revision applicability, flagging missing information for clarification. Integrate source systems and publishing tools through APIs and scripts, providing reviewers with clear changes and supporting evidence before publication. Use recurring exceptions and reviewer feedback to impro

About Sapience AI Sapience AI is the collective intelligence platform for professional communities. We sit above the CRMs, AMS platforms, and knowledge bases that organizations already run, and we turn the expertise scattered across them into something every member can search, act on, and share. The intelligence a community needs is already inside it. Most organizations just cannot reach it. Knowledge lives in silos, in legacy systems, in the heads of a few experts, and in fragmented records no one can connect. We change that. Our work is grounded in four commitments: technology elevates people and never replaces them, the best expertise is already inside the community, everything is built on trust, and every deployment is purpose-driven for the organization it serves. Let’s achieve more, together. Where this role sits This role owns how Sapience AI serves intelligence at scale. You build and run the inference infrastructure that turns the models and reasoning behind MINERVA and COGENT into fast, reliable, affordable answers for members. You work where models meet production: serving, scaling, latency, and cost. Every time a member asks the platform something, your systems are what make the answer arrive quickly and hold up under load. You sit close to applied AI, research, and platform engineering, and you make the difference between a model that works in a notebook and one that serves a community reliably. Why this role exists Collective intelligence is only useful if it is fast and dependable. Members will not wait, and communities cannot rely on a platform that buckles under load or costs too much to run. Serving modern AI at scale is a hard systems problem: large models, tight latency budgets, expensive hardware, and demand that spikes. It takes real infrastructure engineering to get right. The AI/ML Infrastructure and Systems Engineer owns that problem. You make inference fast, reliable, and affordable, so the intelligence the platform promises actually reache

Sapience AI is the collective intelligence platform for professional communities. We sit above the CRMs, AMS platforms, and knowledge bases that organizations already run, and we turn the expertise scattered across them into something every member can search, act on, and share. The intelligence a community needs is already inside it. Most organizations just cannot reach it. Knowledge lives in silos, in legacy systems, in the heads of a few experts, and in fragmented records no one can connect. We change that. Our work is grounded in four commitments: technology elevates people and never replaces them, the best expertise is already inside the community, everything is built on trust, and every deployment is purpose-driven for the organization it serves. Let’s achieve more, together. Where this role sits This role builds the structured knowledge that collective intelligence reasons over. You own the KO (knowledge object) graph: the layer that turns a community’s scattered expertise into connected, queryable knowledge the COGENT architecture can use. You work where messy real-world data becomes trustworthy structure: ingesting, resolving, connecting, and modeling knowledge so reasoning has something solid to stand on. You partner closely with neuro-symbolic AI and applied AI, and you are the reason the platform can answer questions that span a community’s knowledge instead of isolated documents. Why this role exists Language models are fluent, but fluency is not knowledge. To reason over a community’s expertise with rigor, the platform needs that expertise structured, connected, and trustworthy, not just retrieved as text. Building a knowledge graph from real, fragmented sources is hard: entities to resolve, relationships to infer, quality to enforce, and provenance to preserve. The graph is only as good as the engineering behind it. The AI/ML Data and KO Graph Engineer builds that foundation. You turn scattered knowledge into a graph the COGENT architecture can reason

Sapience AI is the collective intelligence platform for professional communities. We sit above the CRMs, AMS platforms, and knowledge bases that organizations already run, and we turn the expertise scattered across them into something every member can search, act on, and share. The intelligence a community needs is already inside it. Most organizations just cannot reach it. Knowledge lives in silos, in legacy systems, in the heads of a few experts, and in fragmented records no one can connect. We change that. Our work is grounded in four commitments: technology elevates people and never replaces them, the best expertise is already inside the community, everything is built on trust, and every deployment is purpose-driven for the organization it serves. Let’s achieve more, together. Where this role sits This role owns the heart of how Sapience AI reasons. You lead the neuro-symbolic approach that combines symbolic knowledge, structure, and rules with neural and language-model capability, the approach that lets the platform reason over a community’s expertise with both flexibility and rigor. You own the direction of the COGENT architecture: how it fuses the KO (knowledge object) graph and symbolic reasoning with neural models, how it stays trustworthy and explainable, and how it advances over time. You lead a specialized team and partner across research, applied AI, and knowledge graph engineering to turn a hard idea into a working system at the center of MINERVA. Why this role exists Pure neural systems are fluent but can be confidently wrong, and they struggle to explain themselves. Professional communities cannot act on answers they cannot trust or trace. Collective intelligence needs reasoning that is both flexible and grounded. Bringing symbolic structure and neural capability together is one of the hardest and most valuable problems in applied AI. It is also the core of what makes Sapience AI different. The Head of Neuro-Symbolic AI owns that problem. You set the

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE Baseten's Compute org is in hyper growth. As it scales, the systems and workflows that keep supply and demand balanced across our GPU fleet need to get more sophisticated, and this role exists to make sure they do. Compute sits at the center of how Baseten allocates, forecasts, and manages the capacity that powers every customer inference request. The team that supports this work, C3, runs on a mix of internal tooling, manual processes, and systems that haven't fully kept pace with the scale of the problem. This role exists to close that gap. You'll design, build, and ship AI-powered workflows that give the Compute and C3 teams real leverage, automating the manual, repetitive, and error-prone parts of the capacity lifecycle so the team can focus on judgment calls that actually need a human. We want someone who can walk in, audit what exists today, identify what's missing or broken, and start shipping fast. You know when to reach for an existing internal tool and when to build something custom in Claude Code. You think two to three steps ahead about how the thing you build today fits into the broader capacity systems architecture tomorrow. And you bring a point of view on our stack, on what we should be building, and on where AI can do something existing tooling simply can't. RESPONSIBILITIES Ship AI-powered workflows for Compute and C3 : build the agents and automations that give capacity analysts, ops leads, an

About Snorkel Snorkel AI is the frontier AI data lab, helping teams build the data and environments behind high-performing frontier and agentic AI. We combine technology with research-driven AI data development to create datasets, benchmarks, evals, and custom solutions for real-world AI systems. Founded out of the Stanford AI Lab in 2019, Snorkel works with leading AI labs and enterprises to move from better data to better outcomes. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler! About The Team Snorkel's AI Platform organization builds the infrastructure and systems that power AI development at scale - synthetic data generation, evaluation, agentic workflows, simulation environments, LLM infrastructure, and distributed compute. Our platform enables engineering and research teams to rapidly experiment with models and agents, measure their behavior, and turn successful experiments into reliable production systems. We're a small team operating at the intersection of distributed systems and applied AI, and we're in the middle of a foundational shift toward agent-first workflows where models interact with tools, environments, data, and other agents over long-running trajectories. The systems we build need to make these inherently non-deterministic workloads observable, reproducible, measurable, and scalable. You will help define how we do that. About The Role We're looking for Software Engineers at Senior / Staff levels who combine strong software and distributed systems fundamentals with experience operating AI systems in production. You'll build the infrastructure that lets teams create, experiment with, evaluate, and operate LLM and agentic workloads at significant scale - from synthetic data and evaluation pipelines to simulation environments, orchestration systems, and LLM infrastructure. You'll work on systems where correctness is not defined by a single deterministic output. Instead, you'll build the infrastructure neede

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE Are you the person on your team who builds the agent everyone else ends up using? We're looking for an AI Engineer to join our Training Product team and do that at Baseten. You'll build AI-driven product features for the customers training and post-training frontier models on our platform, and you'll raise the ceiling on how Baseten itself uses AI internally, turning manual workflows into agentic ones that make every other team faster. You'll work directly with our research engineers to scope and build products, taking ideas from a research loop that already works internally to something customers can run themselves. This is a hands-on role with real autonomy. You'll pick the problems worth solving, build the harnesses, execution flows, and guardrails that make AI systems reliable, and own the results. If you've been shipping agents and want that to be the job, let's talk. EXAMPLE INITIATIVES Take a look at these blog posts written by members of our team: Baseten Training: an autoresearch substrate Introducing Baseten Loops Harnesses are everything. Here's how to optimize yours. Building with NVIDIA Nemotron 3 Ultra and LangChain Deep Agents Code on Baseten RESPONSIBILITIES Build and ship agentic product experiences, including chat-style and assistant-like interfaces, from prototype to GA. Design the harnesses, execution flows, and guardrails that make AI systems reliable in production. Build internal automation

Scale GP is Scale's enterprise Generative AI platform—APIs and infrastructure for knowledge retrieval, inference, evaluation, and intelligent automation. We power mission-critical workflows for leading enterprises, helping teams turn complex data and models into reliable, production-ready AI systems. We're building a new AI Enablement team to create the next generation of agent-powered tools that ground AI in real operational workflows. Our goal: help internal teams demystify their own workflows, then deploy agentic systems that reason over data, take action, and deliver measurable outcomes. We don't build in a vacuum. You'll use our own platform to solve real business problems internally—then selectively commercialize that same stack for customers. What we run on is what we sell. This is a 0→1 team. We're looking for a sharp, product-minded engineer who thrives in ambiguity, moves fast, and loves building systems from scratch alongside customers and cross-functional partners. You'll work closely with product, forward-deployed engineers, data scientists, and applied AI teams to turn real-world problems into scalable production solutions. If you like shipping fast, owning outcomes, and working across the stack—from polished frontends to distributed backends to LLM integrations—this role is for you. What You’ll Do Own full-stack features and projects end-to-end — from design through production deployment — within a larger product area Sample surfaces - Accounting Agents, Finance Copilots, GTM Agents, Agentic Experimentation Platforms Develop reliable backend services in Typescript/Python, work with distributed systems, data pipelines, and AI/ML infrastructure Integrate LLMs, vector databases, and agentic frameworks to power intelligent workflows Ship quickly through tight experimentation loops while maintaining high quality and reliability Adapt across the stack and learn new tools as needed to solve real problems end-to-end Ideal Experience 3+ years of full-tim

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE Baseten's engineers want to work in an AI-first way. What's missing isn't enthusiasm — it's the platform underneath it. Today everyone assembles their own agent config, context files, and MCP servers, so the good patterns stay trapped in individual setups instead of becoming defaults everyone inherits. You'll build that platform: the agent configurations tuned to our monorepo, the context and tooling layer that makes agents competent in our codebase, the evals that tell us which approaches actually work, and the rollout mechanics that get a new engineer productive with agents in week one. You are not here to mandate how engineers use AI — you're here to make the good path the easy path. Success looks like teams adopting what you build because it beats what they'd cobble together themselves, not because a policy requires it. Platform engineer, not AI evangelist. Ship infrastructure, measure it, kill what doesn't work, let adoption be the referee. The playbook for AI-first SDLC doesn't exist at any company yet. You'll write ours. EXAMPLE INITIATIVES Agent substrate — Repo-level context infrastructure that makes agents competent in our codebase ( CLAUDE.md/AGENTS.md conventions, architecture and domain context, and the tooling to keep it accurate as code moves). Internal MCP servers giving agents scoped access to CI, observability, incident tooling, deployment state, and docs. Shared skills, subagents, and hooks th

About Us Pearl is AI for professional services at global scale, combining advanced AI with verified human expertise to deliver help that is accurate, accountable, and fast. Since 2003, our network has connected millions of customers with licensed professionals across 196 countries, making real expertise available anytime, anywhere. Our Values Data driven: Start with truth, measure what matters. Courageous: Bias to action; run toward hard problems. Innovative: Seek novel, elegant solutions. Lean: Do more with less. Build, ship, learn fast. Humble: Strong opinions, lightly held. About the Role Analytics is moving beyond dashboards. We’re building intelligent systems that let analysts and business users explore data, automate analysis, design scalable architectures, and get insights faster and we need a senior engineer to build the data foundations and AI-powered tools that make that possible. This is a hands-on builder role at the intersection of analytics engineering and applied AI. You'll own the pipelines, datasets, and metrics that the business runs on, and you'll build the LLM-powered applications, copilots, agents, natural-language data experiences that sit on top of them. You'll work directly with product, growth, and business teams on real B2C and customer data. What You’ll Do Analytics Engineering & Business Analytics Build and own scalable data pipelines, transformations, analytical datasets, and reusable data products using SQL and Python. Analyze B2C, product, and customer data to identify trends, performance drivers, and actionable insights. Translate business and product questions into reliable metrics and decision-support solutions that help teams measure performance and understand customer behavior. AI-Powered Analytics Design, build, and deploy LLM-powered applications, agents, and analytics workflows using RAG, tool calling, MCP, and modern agent frameworks. Develop reusable AI tools that enable analysts and business user

The AI Retrieval team powers the intelligence behind Asana's AI features by finding relevant work graph content and delivering it to LLM context windows. Our work enables AI features that truly understand your work—both within and outside of Asana—and use that understanding to take action. We also own Asana's traditional search experience. As a Senior Software Engineer on the AI Retrieval team, you'll build the systems that make Asana's AI smart and responsive. You'll tackle challenging problems in search and retrieval, working to improve the speed, cost-efficiency, and quality of our systems while expanding their capabilities to new data sources. This role is based in our New York City office with an office-centric hybrid schedule. The standard in-office days are Monday, Tuesday, and Thursday. Most Asanas have the option to work from home on Wednesdays. Working from home on Fridays depends on the type of work you do, and your recruiter can share more about the in-office requirements. What you'll achieve Reduce the latency and cost of our retrieval system, making Asana's AI features faster and more efficient Improve the quality and relevance of search results to help users find exactly what they need Expand the retrieval system's capabilities to query new Asana objects and third-party data sources Build and optimize search infrastructure using OpenSearch/ElasticSearch Contribute to ML-powered features like embeddings-based retrieval and semantic search Collaborate with cross-functional partners in New York City while building strong relationships with your Warsaw-based peers About you 6+ years of experience writing code in a production environment Curiosity about how AI and technology can solve problems and a desire to use it regularly in your work Hands-on experience in search engineering, including with OpenSearch or ElasticSearch Experience with embeddings and machine learning approaches to search and retrieval Experience in an Applied AI role is a plus Dem

Why RoboForce RoboForce is an AI robotics company developing Physical AI–powered Robo-Labor for dull, dirty, and dangerous work. The company’s robots are engineered for demanding industrial environments, with a focus on real-world deployment and scalability. We are hiring a Lead AI Native Engineer to build RoboForce’s vertical agents and shared agent foundation. Reporting to the co-founder, you will turn frontier models into systems for strategy and engineering. This is a hands-on technical leadership role: you will write code, set architecture, and enable agent development—not lead People programs or organizational transformation. Responsibilities Build vertical agents. Own end-to-end agents for high-value workflows across research, software, hardware, data, and operations—from problem definition through production use. Establish the agent foundation. Build reusable primitives for models, tools, orchestration, context, memory, retrieval, permissions, human approval, and long-running execution. Create the context layer. Connect agents to trusted data through APIs, pipelines, MCP servers, and integrations with clear provenance and access control. Own evaluation and reliability. Build benchmarks, regression tests, tracing, monitoring, and failure-analysis loops across quality, latency, cost, security, and resilience. Advance frontier agent usage. Evaluate new models, coding agents, SDKs, and patterns, then turn useful capabilities into maintainable systems rather than demos. Support strategic initiatives. Help company leadership apply agents and analytical systems to market and customer intelligence, partnerships, fundraising, diligence, scenario analysis, and executive decisions. Provide technical leadership. Set architecture and engineering standards, review designs and code, and create reusable patterns for the technical team. Requirements 5+ years in software engineering, applied AI, ML systems, or a related field, with strong zero-to-one technical judgment. Exper