To succeed in the Connectly interview process, you must demonstrate a unique blend of deep technical execution, architectural foresight, and product-minded execution. We evaluate candidates across several core dimensions to ensure alignment with our high-performing team.

Technical Rigor & OOP – You must have a masterful command of Python and object-oriented design. We do not just look for code that works; we look for code that is modular, easy to read, properly encapsulated, and thoroughly tested. Be ready to write production-grade code under timed conditions.

System-Level Thinking – You should be comfortable designing distributed systems that operate at scale. This means understanding how to use message brokers like Kafka to manage asynchronous workflows, and knowing when to leverage relational databases versus NoSQL solutions for optimal performance.

Product & AI Curiosity – Working at Connectly requires a strong interest in how AI can drive real business outcomes. You should be prepared to discuss how you balance technical complexity with the user experience of a conversational interface, showing a genuine curiosity for the evolving LLM landscape.

Startup Adaptability – We move fast and operate with a high degree of autonomy. You need to demonstrate a track record of navigating ambiguity, managing your own workstreams, and making pragmatic trade-offs between rapid feature delivery and architectural purity.

The interview process at Connectly is designed to be highly practical, thoroughly assessing your engineering capabilities through real-world scenarios rather than academic puzzles. We value your time and aim to keep the process transparent, rigorous, and highly communicative.

The journey typically begins with an initial conversation with a technical lead. This round focuses on your background, your experience building scalable backend systems, and your alignment with our technical stack and culture. Following this, you will proceed to hands-on technical evaluations. This includes a deep-dive coding assessment where you will write clean, production-ready code, and may also involve take-home assignments or technical design reviews to evaluate how you structure solutions to complex business problems.

The timeline above outlines the standard progression of our engineering interview loop. While the exact steps may vary slightly depending on the level of the role and the team, you can expect a highly structured evaluation that respects your schedule. Use this timeline to pace your preparation, focusing first on core coding fundamentals before diving into system architecture and conversational AI concepts.

To help you focus your preparation, we have broken down our core technical evaluation areas into detailed competencies. Understanding these areas will help you align your performance with what our interviewers are looking for.

Object-Oriented Programming (OOP) in Python

Our coding evaluations place a heavy emphasis on clean code, domain modeling, and testing. You will be asked to build a fully functioning system within a limited timeframe, requiring you to write code that is not only correct but also maintainable and extensible.

Be ready to go over:

• Domain Modeling – How to translate complex business rules (like game logic or order processing) into a clean hierarchy of classes and methods.
• Input Validation – Writing robust validation logic to ensure your methods handle unexpected, malformed, or malicious inputs safely.
• Unit Testing – Structuring and writing unit tests rapidly to verify the correctness of your core business logic and edge cases.
• Advanced concepts (less common) – Abstract base classes, custom decorators for validation, and memory-efficient data structures in Python.

Example scenarios:

• "Design a fully functional card game engine in Python that supports drawing, shuffling, scoring, and automated player decisions, complete with input validation and tests."
• "Implement a state machine that manages the lifecycle of a retail order, ensuring state transitions are strictly validated and logged."

Distributed Systems & Data Architecture

At Connectly, we process millions of conversational events daily. You must demonstrate that you can design robust, fault-tolerant architectures that scale horizontally.

Be ready to go over:

• Asynchronous Message Processing – Leveraging Kafka to ingest, queue, and process high-volume events asynchronously.
• Database Selection – Choosing the right storage engine (e.g., Postgres for transactional integrity, DynamoDB for high-throughput, low-latency key-value access).
• Caching & Rate Limiting – Implementing caching strategies and rate limiters to protect downstream services and external APIs from being overwhelmed.
• Advanced concepts (less common) – Event sourcing patterns, distributed locking mechanisms, and managing schema evolution in event streams.

Example scenarios:

• "Design a scalable backend system that receives webhooks from WhatsApp, processes them via an AI pipeline, and updates a merchant's inventory system in real-time."
• "Explain how you would handle out-of-order message delivery in a chat application where maintaining chronological order is critical for the AI's context."

Conversational AI & Agent Orchestration

As an engineer building the future of conversational commerce, you need to understand how to integrate LLMs and AI pipelines into traditional software architectures.

Be ready to go over:

• Agent Workflows – Designing pipelines that orchestrate calls to LLMs, retrieval systems, and internal APIs to generate coherent, context-aware responses.
• Prompt Engineering & Evaluation – Techniques for structuring prompts, managing system instructions, and evaluating the quality of model outputs at scale.
• Latency Mitigation – Strategies for optimizing response times, such as streaming token delivery, parallelizing API calls, and pre-fetching context.
• Advanced concepts (less common) – Retrieval-Augmented Generation (RAG) optimization, vector database indexing, and fine-tuning evaluation loops.

Example scenarios:

• "Design an architecture for a shopping assistant bot that can search a product catalog, check real-time stock levels, and generate a personalized recommendation."
• "How would you build a fallback mechanism for when an LLM fails to return a valid JSON payload required by a downstream API?"

As a Software Engineer at Connectly, you will have a direct hand in shaping our core product offerings and scaling our technical infrastructure. Your day-to-day work will bridge the gap between complex backend engineering and cutting-edge AI product development.

You will design, build, and launch production-grade conversational AI solutions for enterprise clients. This ranges from rapidly prototyping new features to deploying highly scalable backend systems that handle heavy traffic volumes. You will write clean, maintainable code in Python and manage cloud infrastructure using AWS, ensuring our databases (Postgres and DynamoDB) and messaging pipelines (Kafka) are optimized for maximum reliability and low latency.

Collaboration is central to this role. You will work closely with product managers, designers, and enterprise partners to define user journeys, optimize conversational performance metrics, and drive measurable business results for our clients. Additionally, as a senior member of our distributed engineering team, you will mentor other engineers, participate in architectural design reviews, and establish engineering best practices across the organization.

We are looking for exceptional engineers who possess a strong technical foundation and a passion for building high-impact products in a startup environment.

• Must-have skills – 5+ years of hands-on software engineering experience, with a proven track record of building and scaling enterprise systems. High proficiency in Python and deep experience with relational and non-relational databases like Postgres and DynamoDB. Solid understanding of distributed messaging systems, specifically Kafka, and cloud infrastructure on AWS.
• Nice-to-have skills – Experience with frontend engineering technologies such as React and TypeScript. Prior experience developing or deploying conversational AI applications, prompt design, or LLM-based agent evaluation. Experience working in fast-paced, customer-centric startup environments.
• Education – BS or MS in Computer Science, Engineering, or a related technical field (or equivalent practical experience).

Q: How deeply should I prepare for the Python coding round? A: Very deeply. The 75-minute coding round requires you to build a complete, working system (such as a card game) using clean object-oriented Python. You must write modular classes, implement strict validation methods, and write actual unit tests within the timed session.

Q: Does Connectly use take-home assignments in the hiring process? A: Depending on the role and location, we may utilize take-home assignments to evaluate your real-world coding and architectural style. If assigned, expect to walk through your code in detail during the subsequent interviews, explaining your design choices and trade-offs.

Q: What is the hybrid work policy at Connectly? A: We operate as a hybrid company with physical offices in Bogota and San Francisco, and a highly collaborative, remote-first culture everywhere else. We prioritize impact and clear communication over physical presence.

Q: What differentiates candidates who receive offers from those who do not? A: Successful candidates demonstrate a balance of strong technical rigor and high product sense. They write exceptionally clean, production-grade code, write comprehensive tests, and can explain the business impact and user experience implications of their technical decisions.