“Jose is a wonderful asset to any team and is a consummate team player. His energy, passion to learn, and most importantly be a leader and lead new initiatives is outstanding. When you assign a task to Jose, you know it gets done. Jose has a rare combination of technical skills with excellent communication skills that puts even a non-technical users at ease when conversing with him on the most difficult technical aspects of the project. I always loved the fact he took the most difficult technical concepts and distilled it down to a few powerpoint slides during his presentations and leave the entire audience wanting to sign up for the service on the spot! He had a very infectious energy that he brought every day to work that is invaluable at any workplace. It would be a pleasure to have Jose on my team or any team as a matter of fact as I know he will prove to be an excellent leader in whatever he does. I wish him all the best in advancing his career to greater heights.”
About
Technology leader with extensive experience designing, developing, installing and…
Experience
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Education
-
-
-
-
-
Licenses & Certifications
Volunteer Experience
-
-
Organizer
DevOpsCT
- Present 8 years 11 months
Science and Technology
Publications
Languages
-
English
Native or bilingual proficiency
-
Spanish
Native or bilingual proficiency
Recommendations received
1 person has recommended Jose
Join now to viewExplore more posts
-
Madhur Prashant
An agent has no consistent definition. You can think of an agent as an autonomous or semi-autonomous system that can take actions on behalf of the user in a given environment, state, and make decisions or take actions that can accomplish certain tasks along a given time-frame, either by calling tools, dynamically selecting the next action to take or use a deterministic "workflow" (systems where LLMs and tools are orchestrated through predefined code paths). Using an Agent framework that gives you the ability to built systems in both, a reliable and a dynamic way can accelerate your agent development journey using agent abstractions. This means building Agentic systems that can reliably call tools, store memory (episodic/semantic/procedural), have comprehensive logging and observability, human in the loop workflows and the ability to build various multi-agent patterns flexibly based on your use case. A successful Agentic system in production is usually a combination of both, dynamic and predictable/reliable multi-agent systems. Strands Agents SDK gives you exactly these capabilities by treating each “agent” as a combination of a foundation model plus a suite of tools. You define a prompt and register your tools (decorated functions) in code, then Strands handles reasoning→planning→tool-execution cycles, local testing, and cloud deployment (ECS, Fargate, Lambda, EC2), along with support for all other agent abstractions provided above. Excited to share a hedge-fund analyst multi agent system: This uses the newest Anthropic's Claude 4 Sonnet/Opus that powers the Lead Analyst Agent, routing incoming queries to specialized sub-agents for fundamental, technical, and market analyses. Each specialist is wrapped as a callable tool (using the “agents-as-tools” multi-agent pattern), so the orchestrator never has to implement domain logic itself and can handoff the task to an agent as a tool. For sensitive operations (insider lookups), we utilize a HITL approval step that halts execution until a human grants consent. We also use meta-tooling that enables the Lead Analyst to generate, load, and invoke new custom tools at runtime—whether it’s a portfolio beta calculator or a pricer—without redeployment. Strands also embeds observability (Langfuse) and OpenTelemetry tracing so you can trace reasoning events, tool invocations, errors, and end-to-end workflows in real time. View more information on the code implementation here: https://lnkd.in/gJmwVyGi Code implementation: https://lnkd.in/gzTtJvJq Thanks to 🏄♂️ Cagatay Cali for being a reviewer/collaborator on this! Feel free to try it out and reach out with any questions/ideas. #aws #agenticAI #strands #agents #generativeAI
6 Comments -
Walter Thompson
Two of the three co-founders of Operant AI — CTO Priyanka Tembey and CMO Ashley Roof — joined me on Fund/Build/Scale to talk about building the first runtime application protection platform and navigating the challenges of cloud-native security. The company announced a $10M Series A in September 2024 — In October, we discussed taking the leap into entrepreneurship, the lessons they learned through customer discovery and education, making early hires count, and the importance of early-stage team dynamics. RUNTIME: 48:14 EPISODE BREAKDOWN (2:28) “ I think it was within six months that we were able to have our first customer conversation.” (3:46) What can customers do with a runtime application protection platform? (9:38) “ What is interesting about our team is we don't come from prior security vendor companies.” (12:59) Ashley explains how her first “real job” at Google eventually led her to Operant AI. (15:51) “ I had many years of imposter syndrome to get over.” (17:57) Why working with design partner teams is key, particularly for stealth startups. (20:43) Priyanka discusses the company’s early customer education efforts. (24:23) Ashley on why previous runtime application protection products hit “the trough of despair pretty fast.” (28:26) Turning design partners into paying customers was part of their seed-to-Series A transition. (31:04) Priyanka explains how Operant AI runs proof-of-concept programs for customers. (34:28) Why they decided to start up in stealth. (36:29) “ I honestly don't know how any consumer company could possibly start in stealth.” (38:35) “ We have the technical leadership in place now… to scale the product further.” (41:02) Inside their recruiting strategy and process. (44:35) What’s changed since closing the Series A? To hear our conversation, scan one of the QR codes below 👇🏾👇🏾
2 Comments -
Mike Dillinger, PhD
KGC 2025 -- the Knowledge Graph Conference in New York City last week (see knowledgegraph.tech) -- was simply terrific. Great community. Great speakers. Great discussions. The is post 2 about my talk, which focused on the fact that some approaches to systematizing and storing knowledge are less expressive or informative. They rely mostly on human users' intelligence to fill in meaning -- even though some still call them "knowledge graphs". Other approaches are more expressive and pack much more structured knowledge into richer knowledge graphs so AIs can reason more reliably and more autonomously. Knowledge graph relations, for example, can have generic labels and no other information – or they can be much richer and more expressive. These more expressive relations are very clearly valuable assets under management. As our knowledge resources have become richer, with more relation types, they have created many new insights and visibly improved a wide range of services – search, ads targeting, recommendations, insights – at many organizations. The practical Expressivity Checklist for Relations will help you track and plan your efforts to create more expressive knowledge graphs. Ping me if you want to know more : )
3 Comments
Explore top content on LinkedIn
Find curated posts and insights for relevant topics all in one place.
View top content