4‐4 Agentic AI jobs - terrytaylorbonn/auxdrone GitHub Wiki
26.0410 Lab notes (Gdrive) Git
This page posts notes (job ads).. the main goal is to get an idea of what's happening in the field. Whats being used.
JOB SUMMARY: " AI-native, agentic intelligence platform built from the ground up to operate at planetary scale – transforming vast streams of geospatial data into predictive signals that matter"
is forging the new frontier of spatial intelligence, helping decision makers and operators navigate what’s happening now and shape what’s coming next.
About the job Vantor is forging the new frontier of spatial intelligence, helping decision makers and operators navigate what’s happening now and shape what’s coming next. Vantor is a place for problem solvers, changemakers, and go-getters—where people are working together to help our customers see the world differently, and in doing so, be seen differently. Come be part of a mission, not just a job, where you can: Shape your own future, build the next big thing, and change the world.
To be eligible for this position, you must be a U.S. Person, defined as a U.S. citizen, permanent resident, Asylee, or Refugee.
Export Control/ITAR: Certain roles may be subject to U.S. export control laws, requiring U.S. person status as defined by 8 U.S.C. 1324b(a)(3).
Please review the job details below.
Team Overview
Insights is redefining how the world understands what’s happening on Earth. We are an AI-native, agentic intelligence platform built from the ground up to operate at planetary scale – transforming vast streams of geospatial data into predictive signals that matter. Powered by cutting-edge Google Cloud infrastructure, frontier Gen AI models, and collaboration with Google Research on next-generation Earth AI, we are pushing the boundaries of how AI can help enterprise see, reason about, and act on.
Our mission is clear: deliver decision superiority in moments that matter. From national security to global enterprise operations, our platform provides contextual spatial awareness and anticipatory threat detection for customers operating in high-stakes environments. This is not incremental AI – this is intelligence engineered for global impact.
Responsibilities
• Design, develop, and deploy AI-native software products that transform large-scale geospatial data into actionable intelligence and mission-critical insights.
• Implement multi-agent workflows using modern orchestration frameworks (e.g., Google ADK, LangChain, LangGraph) to enable autonomous reasoning, planning, and execution.
• Integrate state-of-the-art Large Language Models (GPT-4, Claude, Gemini, etc.) to power contextual analysis, hypothesis generation, and adaptive decision-making.
• Engineer agents capable of dynamic tool use, structured reasoning, and iterative self-refinement to improve insight quality over time.
• Develop robust data ingestion and transformation layers to support pattern-of-life, detection, anomaly identification, and predictive analytics
• Ensure secure, scalable integrations across cloud and enterprise environments.
• Create feedback loops and reinforcement mechanisms to iteratively improve model reliability and operational trustworthiness.
• Deploy and operate AI systems in production using modern DevOps practices (containerization, orchestration, CI/CD).
• Leverage AI development agents (e.g., Codex, Gemini CLI, Claude Code) as force multipliers for software design, implementation, testing, and documentation.
• Contribute to shared engineering standards, documentation, and best practices for AI-first development.
Minimum Qualifications
• Bachelor's degree in Computer Science, Systems Engineering, Software Engineering, or a related field. Advanced degrees are preferred but not required.
• Must be a U.S. Citizen
• 3+ years of proven experience in a relevant technology domain, with preference for Software engineer and AI/ML
• ‘Strong understanding of technical concepts related to managing cloud-based data and machine learning pipelines (e.g., AWS or GCP)
• Significant software development experience with Python, JavaScript, or similar
Preferred Skills
• Familiarity with vector databases, embeddings, and retrieval-augmented generation(RAG) architectures.
• Knowledge of distributed systems design and high-availability architectures supporting global-scale workloads.
• Leverages AI for team velocity and upskilling, including using AI-assisted development heavily, prototyping quickly, automating repetitive engineering tasks and moving faster than traditional SWE teams.
• Excellent communication and interpersonal skills
• Ability to work independently and collaboratively with remote and/or geographically distributed teams
• Ability to work effectively in a fast-paced, dynamic environment and manage multiple priorities simultaneously
In 2024-2025 I did agentic demos (mainly LangChain). In 2026 I am shifting my focus back to agentic AI, but with a new "twist". Right now (while in Taiwan) mainly planning out strategy with GPT.
2 recent (draft, WIP) Substack posts about agents in AIP:
- https://ziptieai.substack.com/p/68-what-is-an-ai-platform
- https://ziptieai.substack.com/p/69-the-future-of-ai-platforms-aips
MISSING AGENTIC LINKS (there are probably more):
- https://github.com/terrytaylorbonn/auxdrone/wiki/AI-agents
- https://github.com/terrytaylorbonn/auxdrone/wiki/4.0-AI-agents
YTs, articles:
- AI HW dev over decades: https://www.youtube.com/watch?v=2LcxRQJuA54 [Artificial Intelligence] Hardware Trends in Deep Learning | AI Explosion Doesn't Rely on Moore's... Best Partners TV[Artificial Intelligence] Best Partners TV
- 26.0309 https://www.zerohedge.com/medical/ai-beats-human-research-teams-crunching-medical-data
- https://www.youtube.com/watch?v=UabBYexBD4k "is RAG still needed?" (IBM)... answer ... maybe yes....
- https://youtu.be/cqDQV5g7zHo Don't Waste 2026 on the Wrong Career (ML vs AI Engineer) Zen van Riel
- https://youtu.be/PEFso88LkC4 My Honest Thoughts on AI and the Job Market in 2026 (No Hype) Tech With Tim
- https://www.youtube.com/watch?v=d_LbQwoWI7I AI Engineering: A Realistic Roadmap for Beginners Tech With Tim
Recent videos (most are from my favorite AI blogger “Best Partner”) that are very relevant to my agent demos focus.
- 8.1 A VC analysis of the future agent market (it belongs to those who adapt quickly) (#375)
- 8.2 The future of AI belongs to framework tools that maximize AI model reliability (such as Harness) (#377)
- 8.3 An AI guru talks about the “bitter lesson” (bitter for AI gurus) of the unrealized promises of AI (#Welch)
- 8.4 An AI guru who has far more modest expectations (and no empty promises) for AI (#376)
https://www.youtube.com/watch?v=mNhHjkf_ahM 【人工智能】Cursor要凉了么 | 顶级风投杰瑞·默多克警告过时 | 自主Agent | SaaS海啸主浪潮未至 | 编排层革命 | 软件买家剧变 | 基础设施重构 | AI原生公司 | 科技趋势
8.2 The future of AI belongs to framework tools that maximize AI model reliability (such as Harness) (#377)
【人工智能】Agent Harness Engineering | Agent驾驭/管控工程 | 长时任务的缺陷 | 计算机的操作系统 | 通用型和垂直型 | 苦涩的教训 | 工程实践 https://www.youtube.com/watch?v=qua6FfJmydo
8.3 An AI guru talks about the “bitter lesson” (bitter for AI gurus) of the unrealized promises of AI (#Welch)
The following is a quote from Richard Sutton (AI guru) in the recent YT video “Can humans make AI any better?” (from Welch Labs https://youtu.be/2hcsmtkSzIw?t=547 ). This article states that it was a bitter lesson to discover that the “contents” of minds are so “irredeemably” complex (intelligent) that AI can not fake human intelligence.
Sutton's comments (I struggle to understand exactly what he is saying, but basically its sounds like "(1) AI is not intelligent. (2) The answer: Use the same AI tech to search and discover the world by itself)."
Note: Yan LeCun has some harsh words for LLMs. I spent a few months doing various demos of his new JEPA approach to AI. From what I could tell its a rehash of the LLM model concept (just another transformer based UFA). I am still a bit confused by how Yan thinks this will make robots (1) intelligent, (2) able to perceive the world, (3) able to learn autonomously, or (4) safe enough to be allowed anywhere near humans (let alone in the home). Apparently Gemini (below) has been programmed ("trained") solely on text that supports LeCun's ideas.
Andrew Ng is brilliant, soft-spoken and unassuming. This video is full of his brilliant insights (too many to list) on what AI really is. The biggest thing I took from this video is that he originally tried to study the human brain to understand how it hosts intelligence. But the brain was too complex, so he dropped the idea and instead focused on GPU-based binary AI. Such honesty is refreshing. This video is a must watch for anyone who really wants to understand AI.
【人工智能】AGI还早着呢 | 吴恩达 | Agentic AI | 规模化Scaling Laws瓶颈 | 持续学习难题 | 灾难性遗忘 | 开源模型优势 | 赋能人类 | 职业重构 | 行业泡沫
https://www.youtube.com/watch?v=D5rlQiSvbek
