Tag: AI innovation

Careers Blog, Talent Spotlight

Matías Marenchino: Where AI Becomes Impact

Mismo thrives on the strength of its exceptional team. We provide top-tier remote software development teams that seamlessly integrate with companies worldwide, driving exceptional results. Many organizations have partnered with Mismo to augment their development capabilities and achieve their technology goals.

At the heart of these successful teams are the talented individuals who make Mismo what it is. We’re committed to celebrating our remarkable employees and their invaluable contributions to our company culture. Our employee spotlights showcase the genuine relationships we’ve forged with team members and clients, highlighting their dedication and the positive impact they have on our collaborative environment.

 

  • Tell us a little about yourself: Hi, I’m Matias. I’m originally from Córdoba, Argentina, and I’ve been part of Mismo for almost five years. As an MLOps Engineer, my primary focus is supporting our data scientists and helping them seamlessly deploy their machine learning models into production. Outside of work, I’m the proud parent of two wonderful kids, and I enjoy spending my free time playing padel.
  • What initially attracted you to engineering and how have you found inspiration in this career? My journey into engineering actually began with a passion for mathematics. While studying math, I took a Numerical Analysis course that involved programming, and I quickly discovered how much I enjoyed it. That experience sparked my interest in engineering, and today, I find inspiration in the simple yet powerful ability to use technology to solve complex, real-world problems.

  • What innovation or technical advancement have you been a part of that you consider to have made a milestone in your engineering field? One of the milestones I’m most proud of has been designing and implementing robust pipelines that bring machine learning models into production. These models are often developed by data scientists who are exceptionally skilled in their field but may not have extensive software engineering experience. Building the infrastructure that transforms their work into stable, scalable, production-ready services is both a rewarding technical challenge and a meaningful way to create real-world impact.
  • What made you choose Mismo? I was originally drawn to Mismo because of its fantastic atmosphere. It’s a place filled with warm, welcoming people who are always genuinely willing to support one another and help wherever they can.
  • How would you describe the work environment at Mismo? With 20 years of professional experience, I can honestly say that Mismo has the best work environment I have ever been a part of.
  • What learning and development opportunities have you had at Mismo and how have you utilized these opportunities to grow as a professional? I have always been a highly self-taught person, so rather than relying primarily on formal training resources, my greatest learning opportunity at Mismo has been my team. The collaborative environment allows me to continuously learn from my peers and grow professionally by working through day-to-day challenges together.
  • What aspects of working at Mismo’s engineering team make you proud and motivate you in your daily tasks? What makes me most proud and keeps me motivated every day is the team’s unwavering willingness to support one another. Knowing that I have such a supportive group behind me makes all the difference in my day-to-day work.
  • How do you believe the company and engineering can positively impact the world, people’s lives, and make a difference in their respective industries? Engineering certainly has the power to make a positive impact on the world, although the extent of that impact depends greatly on the projects we choose to pursue. Especially today, with the rapid advancement of AI and the uncertainty surrounding where these technologies may lead, it is essential that we focus on building responsible, reliable solutions that serve a meaningful and practical purpose.
  • What Mismo policies or practices have helped you improve your work-life balance? Since I work primarily as a contractor, my work-life balance is largely influenced by the specific projects and expectations set by each client. However, Mismo’s supportive environment and understanding of this dynamic help me navigate those requirements effectively while maintaining a healthy balance.
  • What inspiring advice would you give to someone considering a career in engineering or looking to join a company like Mismo? My advice is simple but essential: study hard, work hard, and always be willing to help your colleagues. Technical skills may open doors, but teamwork and mutual support are what build a successful, meaningful, and fulfilling career over the long term.

  • What tools or technologies do you use most frequently in your daily work as an engineer at Mismo? My day-to-day work revolves heavily around Python development. To orchestrate and manage data workflows, I regularly use tools such as Airflow, Snowflake, and Databricks. More recently, we have also begun leveraging AI tools like Claude and Gemini to enhance our workflows and improve productivity. 
Careers Blog, Engineering

Leveraging Large Language Models

Even if you’re not closely following the advancements in AI and Large Language Models (LLMs), it’s highly likely that you’ve encountered ChatGPT, —whether in the news or through a friend. Instead of hearing, “Hey, let’s Google that”, you may now hear, “Hey, let’s ask ChatGPT”.

Let’s explore this technology and how it has been rapidly integrating into our daily lives, particularly in the workplace and across various activities within companies of all sizes. We’ll conclude with tips and advice on how to use it responsibly while maintaining realistic expectations. Your own workplace may already be taking initiatives to adopt tools or applications powered by LLMs.

First, let’s define what an LLM is: “It is a subset of Generative AI that refers to artificial intelligence systems capable of understanding and generating human-like language” (Chen, 2024, p.5). These generation capabilities are achieved through training on massive and diverse datasets over extended periods using deep learning — a branch of machine learning.

By leveraging layers of neural networks, which employ probability and other techniques to establish associations within unstructured data (e.g. text, images, videos, audio), LLMs generate the most likely response to a given query. Deep learning enables these models to mimic certain aspects of human cognition, processing vast amounts of information and making decisions in a way loosely analogous to how our brain maps memories, knowledge, and electric signals across billions of neurons.

Building on the concept of unstructured data, these models rely on it as the primary input and output for the services and tools increasingly integrated into our daily tasks. Text serves as the main interface for interacting with LLM applications. For instance, these models can summarize transcripts of virtual meetings, detailing what each participant shared, classifying topics discussed, and even suggesting action items— particularly helpful features for those of us who often forget to take notes.

In a broader, organization-wide use cases, LLMs assist with handling questions and answers by combining search functionalities to process queries efficiently. This often involves training a custom model using techniques like Retrieval-Augmented Generation (RAG), which equips the LLM to retain relevant context from an organization’s internal data. Such models can search through indexed company data in vector databases, delivering more accurate and tailored results.

The capabilities of multi-modal models that LLMs have recently improved, allowing the integration of more complex data and interactions. These models can now follow prompts and instructions at a higher level of programmability, enabling outputs such as customized image or video animations. Chatbots powered by LLMs can also generate highly realistic voice responses, making it increasingly difficult to distinguish between a bot and a real person.

All of this sounds fantastic—revolutionary, disruptive, and incredibly innovative.  The hype surrounding LLMs remains high, fueled by the rapid pace of advancements in model performance, framework availability,  and the emergence of new businesses and technologies. And then there’s the ambitious concept of Artificial General Intelligence (AGI), a goal that continues to captivate imaginations, though it carries significant risks and demands cautious exploration.

However, there are important factors to consider when adopting this technology. As end users, we must exercise caution when assigning tasks to any LLMs-powered service or application. One major limitation is the phenomenon of “hallucinations,” where models generate inaccurate or entirely fabricated responses. Even with well-tuned models, results can sometimes be unreliable. Therefore, it’s crucial not to trust these outputs blindly. Much like cross-checking information from multiple sources on Google, we should scrutinize LLM- generated responses and rely on our own domain knowledge and instincts.

To get the best results, turn interactions with LLMs into ongoing conversations rather than relying on one-shot exchanges (known as zero-shot prompts). Providing clear, specific instructions increases the likelihood of achieving accurate and useful outputs. 

What Lies Ahead?
As mentioned earlier, the technological landscape around  LLMs is evolving rapidly. Speaking of AGI, while it may be the ultimate aspiration for some, it’s a goal that requires careful and deliberate steps. When leveraging LLMs, we must prioritize quality, security, privacy and dependability in our professional and organizational activities. LLMs are powerful tools, but responsive use is key to unlocking their full potential.   

Bibliography

Written by:

Alejandro Castillo
FullStack Engineer
Country: Costa Rica