Not every day in a developer's journey involves writing code. Today was dedicated to career development and strategic networking - specifically, connecting with DIIO, a Chilean AI-powered sales technology company that caught my attention for their innovative approach to sales automation and their need for junior developers with AI expertise.
Discovering DIIO
While researching AI companies in Chile's growing tech ecosystem, I came across DIIO through their LinkedIn presence. Their company description immediately resonated with my background in artificial intelligence and data science:
"diio empodera a las empresas revolucionando cómo gestionan y operan sus equipos de ventas, todo ello impulsado por inteligencia artificial avanzada."
DIIO's mission centers on transforming sales team management through advanced AI, acting as an artificial sales expert that ensures salespeople follow company standards while providing data-driven feedback on customer interactions.
What DIIO Does
DIIO's platform addresses several critical pain points in modern sales operations:
AI-Powered Sales Coaching
- Real-time feedback based on customer interactions
- Standards compliance monitoring for sales teams
- Coaching recommendations based on latest consultative selling research
Sales Process Automation
- Automatic CRM updates
- Follow-up email drafting
- Customer conversation record keeping
- Client summary generation
Performance Optimization
- Commitment checklists
- Comparison against custom sales playbooks
- Data-driven strategy refinement tools
The company positions itself as providing Latin American consultative sales companies with an unparalleled competitive advantage in an evolving sales landscape.
The Opportunity: Junior Developer - RAG Systems
The position that caught my eye involves developing RAG (Retrieval-Augmented Generation) systems - a perfect intersection of my academic background and current AI trends. RAG systems combine the power of large language models with external knowledge retrieval, making them ideal for:
- Customer interaction analysis
- Sales playbook integration
- Real-time coaching recommendations
- Automated content generation
Why I'm a Strong Candidate
My experience from Advanced Topics in Artificial Intelligence (IIC3692) at UC provides directly relevant background:
RAG System Understanding
- Vector databases and embedding techniques
- Information retrieval algorithms
- Language model integration and fine-tuning
- Prompt engineering and optimization
Practical AI Implementation
- Experience with modern AI frameworks (transformers, LangChain)
- Understanding of production AI system challenges
- Knowledge of ML model deployment and scaling
Software Development Foundation
- Full-stack development capabilities
- Database design and optimization (crucial for RAG systems)
- API development and integration
- Performance optimization and monitoring
The Strategic Approach
Rather than just submitting a standard application, I decided to take a more strategic approach:
1. Direct Recruitment Contact
I reached out directly to DIIO's recruitment team through LinkedIn, highlighting:
- Specific interest in their RAG system development role
- Relevant coursework and practical AI experience
- Understanding of their business model and technical challenges
2. Demonstrating Technical Competence
Tomorrow's plan involves building a practical RAG implementation to:
- Refresh my knowledge of current RAG architectures
- Create a tangible demonstration of my capabilities
- Prepare for technical discussions during the interview process
3. Industry Research
Understanding DIIO's competitive landscape and technical challenges:
- AI-powered sales tools market in Latin America
- Current RAG system implementations in business applications
- Integration challenges with existing CRM systems
Tomorrow's Technical Deep-Dive
The next development session will focus on implementing a RAG system from scratch, covering:
Core Components
- Document ingestion and preprocessing
- Vector embedding generation and storage
- Similarity search and retrieval mechanisms
- LLM integration for response generation
Technical Stack
- Vector Database: Pinecone, Weaviate, or FAISS
- Embeddings: OpenAI embeddings or open-source alternatives
- LLM Integration: OpenAI GPT or open-source models
- Framework: LangChain or custom implementation
Use Case Simulation
Building a simplified version of what DIIO might need:
- Sales conversation analysis
- Playbook-based recommendation generation
- Customer interaction summarization
The Bigger Picture
This opportunity represents more than just a job application - it's a chance to:
Apply Academic Knowledge
Translate theoretical AI concepts from university coursework into real-world business solutions that directly impact sales team performance.
Enter Chile's AI Ecosystem
DIIO represents the growing AI startup scene in Chile, offering exposure to cutting-edge technology development in a Latin American context.
Build Specialized Expertise
RAG systems are becoming increasingly important in enterprise AI applications, making this role an excellent stepping stone for AI engineering career development.
Combine Technical and Business Skills
Working at the intersection of AI technology and sales automation provides valuable experience in business-focused AI applications.
Lessons in Career Development
Today reinforced several important principles about career growth in tech:
Proactive Networking
Reaching out directly to companies, especially in emerging fields like AI, often yields better results than passive application submission.
Technical Preparation
Having practical projects and implementations ready demonstrates genuine interest and competence beyond academic credentials.
Strategic Positioning
Understanding a company's specific technical challenges allows for more targeted and compelling candidacy presentation.
The combination of strong academic foundation from UC's Computer Engineering program and practical development skills positions me well for this type of specialized AI role. Tomorrow's RAG system implementation will serve as both technical preparation and demonstration of continued learning commitment.
Next: Building a practical RAG system implementation to demonstrate AI development capabilities and prepare for technical discussions with DIIO's team.