Machine Learning Engineer

About Addi

We are a leading financial platform, building the future of payments, shopping, and banking—a world where consumers and merchants can transact effortlessly and grow together. Today, we serve over 2 million customers and partner with more than 20,000 merchants, making Addi Colombia’s fastest-growing marketplace.

With a state-of-the-art, technology-first approach, we provide banking solutions (deposits, payments, unsecured credit) and commerce services (e-commerce, marketing), bridging the financial gap for millions and redefining how people experience financial freedom. As the country’s leading Buy Now, Pay Later provider, we have secured regulatory approval to operate as a bank, unlocking even greater opportunities for our customers. In the past year, we have also achieved profitability, reinforcing the strength of our business model and our ability to scale sustainably.

Our mission has earned the trust of world-class investors, including Andreessen Horowitz, Architect Capital, GIC, Goldman Sachs, Greycroft, Monashees, Notable Capital, Quona Capital, Union Square Ventures, Victory Park Capital, and more, who back our vision for the future. With their support, we are not just growing—we are transforming Latin America’s financial ecosystem and shaping the next generation to shop, pay, and bank in Colombia.

But what truly sets us apart is how we build. We are a conscious company, driven by deep experience in scaling technology, services and products, and we live by our values every day.

About the Role

This is where you come in. Below, you’ll find what this role is all about—the impact you’ll drive, the challenges you’ll tackle, and what it takes to thrive at Addi. If you’re ready to be part of something big, keep reading.

What’s the mission you’ll drive

Scale Addi’s competitive advantage by building a world-class ML Ops foundation that accelerates the transition from model prototype to production, while ensuring our AI systems from credit scoring to generative agents are resilient, cost-efficient, and seamlessly integrated into our core financial product.

What you will do

• Ensure ML/AI systems can be served reliably in production, maintaining strong operational excellence for availability, latency, and incident response, in partnership with the Data Scientist role for model/agent logic and iteration.

• Build and maintain the serving and integration layer for ML/AI solutions (APIs, connectors, asynchronous execution patterns), enabling seamless integration with internal systems and Ops tooling.

• Establish clear mechanisms for monitoring and reliability of ML/AI systems in production (dashboards, alerts, core KPIs, regression detection, and data/feature quality checks).

• Enable repeatable delivery for ML/AI services through strong engineering practices (CI/CD, testing, release strategies, rollback, and operational runbooks).

• Make contributions to our Architecture Decision Records repository by evaluating and proposing platform upgrades for ML/AI systems (e.g., feature serving patterns, workflow orchestration, scalable storage) to improve reliability, scalability, and reuse.

What we’re looking for

Proven experience in architecting and serving production-grade ML systems

• 4–7 years of experience in software engineering, with at least 3 years focused specifically on ML Ops or Data Engineering in a production environment.

• Demonstrates the ability to design high-availability serving layers using APIs (FastAPI, gRPC) and asynchronous execution patterns to handle high-concurrency fintech workloads.

• Possesses a deep understanding of the "handshake" between data science and engineering, ensuring models are packaged, versioned, and integrated into internal systems without friction.

• Expert-level knowledge of AWS (or similar), Kubernetes, Airflow/Prefect, and Databricks/Spark.

• Track record of implementing request batching and model quantization to balance high-performance throughput with infrastructure costs.

Possesses strong technical fluency in the Python and Data ecosystem

• Exhibits advanced Python engineering skills, moving beyond simple scripting to build modular, testable, and maintainable codebases.

• Expert-level knowledge of core ML libraries (NumPy, Pandas, scikit-learn) and at least one deep learning framework (PyTorch or TensorFlow).

• Solid expertise in data-intensive stacks like Spark or Databricks and the ability to write complex, optimized SQL for feature extraction and data validation.

Experienced in establishing mission-critical observability and reliability

• Has a demonstrated ability to build comprehensive monitoring suites (logs, metrics, traces) that detect not just system downtime, but ML-specific failures like data drift or feature quality regressions.

• Track record of leading incident response and post-mortems, with a focus on reducing Mean Time to Recovery (MTTR) for model-related production issues.

• Proven ability to implement automated alerting and regression detection that prevents degraded models from impacting the end-customer experience.

Demonstrates a mastery of ML orchestration and engineering best practices

• Proven experience in building repeatable CI/CD pipelines for ML (MLOps), including automated testing, canary releases, and seamless rollback strategies.

• Has solid expertise in workflow orchestration tools (e.g., Airflow, Prefect) and storage patterns (Postgres, Vector DBs) required for complex ML lifecycles.

• Experienced in contributing to Architecture Decision Records (ADRs) to standardize feature serving patterns and scalable storage across the engineering org.

Track record of building and scaling AI Agentic systems

• Possesses practical experience with the components of modern AI agents, including RAG (Retrieval-Augmented Generation), orchestration frameworks (LangChain/LlamaIndex), and guardrail implementation.

• Demonstrates an understanding of the unique operational challenges of LLMs, such as token cost management, prompt versioning, and latency optimization.

• Experienced in evaluating and integrating graph-based architectures or graph databases when required for complex data relationship mapping.

Exhibits exceptional cross-functional communication and ownership

• Proven ability to translate highly technical infrastructure bottlenecks into clear business risks or opportunities for non-technical stakeholders.

• Demonstrates an "Ownership Mentality" by taking end-to-end responsibility for the reliability of the ML platform, from the initial architectural proposal to 2:00 AM incident resolution.

• Varies communication style effectively to mentor Data Scientists on engineering best practices while collaborating with Product Managers on roadmap feasibility.

Why join us?

• Work on a problem that truly matters – We are redefining how people shop, pay, and bank in Colombia, breaking down financial barriers and empowering millions. Your work will directly impact customers' lives by creating more accessible, seamless, and fair financial services.

• Be part of something big from the ground up – This is your chance to help shape a company, influencing everything from our technology and strategy to our culture and values. You won’t just be an employee—you’ll be an owner

• Unparalleled growth opportunity – The market we’re tackling is massive, and we’re growing faster than almost any fintech lender at our stage. If you’re looking for a high-impact role in a company that’s scaling fast, this is it.

• Join a world-class team – Work alongside top-tier talent from around the world, in an environment where excellence, ownership, and collaboration are at the core of everything we do. We care deeply about what we build and how we build it—and we want you to be a part of it.

• Competitive compensation & meaningful ownership – We believe in rewarding our talent. You’ll receive a generous salary, equity in the company, and benefits that go beyond the basics to support your growth.

How the hiring process looks like

We believe in a fast, transparent, and engaging hiring experience that allows both you and us to determine if there's a great fit. Here’s what our process looks like:

• Step 1: People Interview (30 min)
  A conversation with a recruiter or hiring manager to get to know you, your experience, and what you're looking for. We’ll also share more about Addi, our culture, and the role.

• Step 2: Initial Interview (45-60 min)
  A more in-depth conversation with the hiring manager, where we explore your skills, experience, and problem-solving approach. We want to understand how you think and work.

• Step 3: Take Home Challenge (5-6 days)
  Complete a simple take-home challenge within a 1-week window. With this technical challenge, we want to see your technical expertise solving a real-world problem. We expect that you invest 5 hours or less in developing a working solution.

• Step 4: Take Home Challenge Review (60 min)
  Meet with a ML Engineer and the ML Lead to talk about your take-home exercise submission and any questions you might have.

• Step 5: Co-Founder Interview
  If there’s a strong match, you’ll have a final conversation with our Founder to align on expectations, cultural fit and ensure mutual excitement. From there, we’ll move quickly to an offer and discuss next steps.

We value efficiency and respect for your time, so we aim to complete the process as quickly as possible. Our goal is to make this experience insightful and exciting for you, just as much as it is for us. Regardless of the outcome, we are committed to always providing feedback, ensuring that you walk away with valuable insights from your experience with us.