📍 Job Overview

Job Title: AI/ML Engineer – Azure (LLM Integrations & Cloud Applications)
Company: Softgic S.A.S.
Location: Bogota, Capital District, RAP (Especial) Central, Colombia
Job Type: Full-Time
Category: AI/ML Engineer
Date Posted: July 21, 2025
Experience Level: Mid-Senior level (3-6 years)
Remote Status: Remote (for Colombia, México, Argentina, Guatemala, Honduras, and Perú residents)

🚀 Role Summary

Design and implement LLM-powered solutions using Azure OpenAI and Azure AI Studio.
Build RAG (Retrieval-Augmented Generation) pipelines using Azure AI Search and Blob Storage for intelligent retrieval.
Orchestrate AI workflows and pipelines using Azure AI Foundry or PromptFlow.
Collaborate cross-functionally with cloud architects, developers, and product stakeholders to integrate AI capabilities with enterprise systems and third-party applications via APIs and SDKs.

📝 Enhancement Note: This role requires a strong background in Azure Machine Learning, prompt engineering, and Python to succeed in designing and implementing LLM-powered solutions and RAG pipelines.

💻 Primary Responsibilities

Solution Design & Implementation: Design and implement LLM-powered solutions using Azure OpenAI and Azure AI Studio, tailoring models to specific business contexts through prompt engineering.
RAG Pipeline Development: Build RAG pipelines using Azure AI Search and Blob Storage to enable intelligent retrieval and enhance AI model performance.
AI Workflow Orchestration: Orchestrate AI workflows and pipelines using Azure AI Foundry or PromptFlow, ensuring high availability, scalability, and performance of AI services in production.
Backend Logic Development: Develop backend logic using Azure Functions, REST APIs, and automation services to support AI integrations and enterprise applications.
Data Pipeline Management: Manage and optimize data pipelines and document storage with Azure Blob Storage, ensuring data integrity and accessibility for AI models.
AI Deployment & Monitoring: Monitor, debug, and maintain AI deployments using Azure monitoring tools, addressing any performance issues or errors that arise.
Cross-Functional Collaboration: Collaborate with cloud architects, developers, and product stakeholders to integrate AI capabilities with enterprise systems and third-party applications via APIs and SDKs.

📝 Enhancement Note: This role involves a high degree of collaboration with various teams, requiring strong communication skills and the ability to work effectively in a cross-functional environment.

🎓 Skills & Qualifications

Education: Bachelor's degree in Computer Science, Artificial Intelligence, or a related field. Relevant coursework in machine learning, natural language processing, and cloud computing.

Experience: 3-6 years of experience in software or AI engineering, with at least 2 years in Azure AI. Proven experience in designing, implementing, and maintaining LLM-powered solutions and RAG pipelines.

Required Skills:

Proficient in Azure OpenAI, LLM (Large language model), prompt engineering, and Python.
Strong experience with Azure AI Search for vector/document retrieval.
Proficient in Azure Blob Storage, Azure Functions, and Azure SDKs.
Proficiency in at least one web framework (e.g., Flask, FastAPI) for backend logic development.
Understanding of cloud-native architecture, access control, and CI/CD pipelines.
Experience building production-grade AI solutions or intelligent app features.

Preferred Skills:

Certifications like Azure AI Engineer Associate or Azure Developer Associate.
Familiarity with tools like LangChain, Semantic Kernel, or Prompt orchestration frameworks.
Experience building copilots, chatbots, or intelligent assistants.
Knowledge of enterprise integrations (e.g., CRM, DMS).
Good practices in version control, testing, and deployment pipelines.
English level: B2+ or higher.

📝 Enhancement Note: While not explicitly stated, having experience with enterprise integrations and good practices in version control, testing, and deployment pipelines would be highly beneficial for success in this role.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

A portfolio showcasing LLM-powered solutions and RAG pipelines implemented using Azure OpenAI, Azure AI Studio, Azure AI Search, and Azure Blob Storage.
Live demos or case studies demonstrating the integration of AI capabilities with enterprise systems and third-party applications via APIs and SDKs.
Examples of prompt engineering and model adaptation for specific business contexts.

Technical Documentation:

Detailed documentation of data pipelines, including data sources, transformations, and storage solutions using Azure Blob Storage.
Code comments and documentation explaining the purpose, functionality, and usage of key components in LLM-powered solutions and RAG pipelines.
Version control and deployment processes, including branching strategies, pull requests, and CI/CD pipelines.

📝 Enhancement Note: Given the collaborative nature of this role, it is essential to include examples of cross-functional collaboration and stakeholder communication in the portfolio.

💵 Compensation & Benefits

Salary Range: To be agreed upon.

Benefits:

Certified as a Great Place to Work.
Opportunities for advancement and growth.
Paid time off.
Formal education and certifications support.
Benefits with partner companies.
Referral program.
Flexible working hours.

📝 Enhancement Note: While the salary range is not specified, research suggests that AI/ML Engineers in Colombia with 3-6 years of experience typically earn between COP 6,000,000 and COP 10,000,000 per month, depending on the company size and industry.

🎯 Team & Company Context

🏢 Company Culture

Industry: Softgic S.A.S. works for the digital and cognitive transformation of clients, focusing on quality, client satisfaction, and continuous professional development.

Company Size: Medium-sized company with opportunities for growth and advancement.

Founded: Softgic S.A.S. was founded with the mission to deliver quality products and services, achieve client satisfaction, encourage professional development, and comply with applicable legal and regulatory requirements.

Team Structure:

The AI/ML team consists of engineers with expertise in Azure Machine Learning, Azure OpenAI, and LLM integrations.
The team collaborates cross-functionally with cloud architects, developers, and product stakeholders to integrate AI capabilities with enterprise systems and third-party applications.
The team follows Agile methodologies, with sprint planning, code reviews, and quality assurance practices in place.

Development Methodology:

Agile/Scrum methodologies with sprint planning for web projects.
Code review, testing, and quality assurance practices to ensure code quality and performance.
Deployment strategies, CI/CD pipelines, and server management to ensure high availability, scalability, and performance of AI services in production.

Company Website: www.softgic.co

📝 Enhancement Note: Softgic S.A.S. places a strong emphasis on quality, client satisfaction, and continuous professional development, creating an environment that values collaboration, innovation, and growth.

📈 Career & Growth Analysis

AI/ML Engineer Career Level: This role is at the mid-senior level, requiring 3-6 years of experience in software or AI engineering, with at least 2 years in Azure AI. The role involves designing, implementing, and maintaining LLM-powered solutions and RAG pipelines, with a focus on prompt engineering and model adaptation for specific business contexts.

Reporting Structure: The AI/ML Engineer reports directly to the AI/ML Team Lead or Manager and collaborates cross-functionally with cloud architects, developers, and product stakeholders.

Technical Impact: The AI/ML Engineer has a significant impact on the development and integration of AI capabilities with enterprise systems and third-party applications, enhancing user experience and driving business value through intelligent retrieval and LLM-powered solutions.

Growth Opportunities:

Technical Growth: Expand expertise in Azure OpenAI, LLM integrations, and cloud applications, with opportunities to specialize in specific domains or technologies.
Leadership Growth: Develop leadership skills through mentoring junior team members, driving projects, and contributing to strategic decision-making processes.
Architecture Growth: Gain experience in designing and implementing scalable, secure, and high-performing AI solutions, with opportunities to work on complex, large-scale projects.

📝 Enhancement Note: Softgic S.A.S. offers opportunities for advancement and growth, with a focus on continuous professional development and a collaborative work environment that encourages innovation and learning.

🌐 Work Environment

Office Type: Softgic S.A.S. has a remote-first work environment, with opportunities for in-person collaboration and team-building activities.

Office Location(s): The company is headquartered in Bogota, Colombia, with remote team members located throughout the region.

Workspace Context:

Remote Work: Remote team members have access to the necessary tools and resources to perform their job functions effectively, including secure remote access to company systems and resources.
Collaboration Tools: The company uses collaboration tools like Microsoft Teams, Slack, or similar platforms to facilitate communication and coordination among team members.
Hardware & Software: Remote team members are provided with the necessary hardware and software to perform their job functions, including laptops, monitors, and access to relevant software tools and platforms.

Work Schedule: The company offers flexible working hours, with a focus on results and productivity rather than strict working hours. Team members are expected to be available during core business hours for collaboration and communication.

📝 Enhancement Note: Softgic S.A.S.'s remote-first work environment offers flexibility and convenience, enabling team members to balance their personal and professional lives while maintaining a strong connection to the company and its mission.

📄 Application & Technical Interview Process

Interview Process:

Technical Screening: A technical assessment focused on Azure Machine Learning, Azure OpenAI, LLM integrations, and Python. Candidates should be prepared to discuss their experience with LLM-powered solutions, RAG pipelines, and AI workflow orchestration.
Cultural Fit Assessment: An interview focused on assessing the candidate's cultural fit with the company, their communication skills, and their ability to work effectively in a cross-functional team environment.
Final Evaluation: A final interview or assessment to evaluate the candidate's overall fit for the role and the company, with a focus on their technical skills, problem-solving abilities, and potential for growth.

Portfolio Review Tips:

Highlight LLM-powered solutions and RAG pipelines implemented using Azure OpenAI, Azure AI Studio, Azure AI Search, and Azure Blob Storage.
Include live demos or case studies demonstrating the integration of AI capabilities with enterprise systems and third-party applications via APIs and SDKs.
Showcase prompt engineering and model adaptation for specific business contexts, with a focus on real-world use cases and problem-solving.

Technical Challenge Preparation:

Brush up on Azure Machine Learning, Azure OpenAI, LLM integrations, and Python, with a focus on designing, implementing, and maintaining LLM-powered solutions and RAG pipelines.
Familiarize yourself with Azure AI Search, Azure Blob Storage, Azure Functions, and Azure SDKs, with a focus on data pipeline management, intelligent retrieval, and AI deployment.
Prepare for questions related to cloud-native architecture, access control, and CI/CD pipelines, with a focus on designing and implementing scalable, secure, and high-performing AI solutions.

ATS Keywords: [See the comprehensive list of ATS keywords at the end of this document]

📝 Enhancement Note: Softgic S.A.S. places a strong emphasis on technical proficiency and cultural fit, with a comprehensive interview process designed to evaluate the candidate's skills, experience, and potential for growth in the role.

🛠 Technology Stack & Web Infrastructure

Frontend Technologies:

Azure OpenAI: Used for designing and implementing LLM-powered solutions, with a focus on prompt engineering and model adaptation for specific business contexts.
Azure AI Studio: A cloud-based platform for building, deploying, and managing AI models and applications, with support for Azure OpenAI and other AI services.

Backend & Server Technologies:

Azure AI Search: A cloud-based search service that enables intelligent retrieval and enhances AI model performance through RAG pipelines.
Azure Blob Storage: A scalable, secure, and durable cloud storage service for data pipelines and document storage, with support for intelligent retrieval and AI model training.
Azure Functions: A serverless compute platform that enables the development of event-driven, compute-on-demand applications, with support for AI integrations and enterprise applications.

Development & DevOps Tools:

Azure AI Foundry or PromptFlow: Tools for orchestrating AI workflows and pipelines, ensuring high availability, scalability, and performance of AI services in production.
Azure DevOps: A comprehensive suite of development, project management, and collaboration tools that support the entire software development lifecycle, from planning and tracking to release and deployment.
Azure Monitoring: A unified monitoring service for Azure resources, with support for AI deployment monitoring, debugging, and maintenance.

📝 Enhancement Note: Softgic S.A.S. leverages the Azure ecosystem for designing, implementing, and maintaining LLM-powered solutions and RAG pipelines, with a focus on scalability, security, and high performance.

👥 Team Culture & Values

AI/ML Team Values:

Innovation: Encouraging continuous learning, experimentation, and the pursuit of cutting-edge AI solutions and technologies.
Collaboration: Fostering a culture of cross-functional collaboration, knowledge sharing, and teamwork to drive business value and enhance user experience.
Quality: Delivering high-quality AI solutions and services that meet or exceed client expectations and industry standards.
Integrity: Upholding ethical standards and best practices in AI development, deployment, and maintenance.

Collaboration Style:

Cross-Functional Integration: Encouraging collaboration between AI/ML engineers, cloud architects, developers, and product stakeholders to integrate AI capabilities with enterprise systems and third-party applications.
Code Review Culture: Fostering a culture of code review, with a focus on code quality, performance, and maintainability.
Peer Programming: Encouraging peer programming and knowledge sharing to drive continuous learning and skill development.

📝 Enhancement Note: Softgic S.A.S. fosters a culture of innovation, collaboration, and quality, with a strong emphasis on cross-functional collaboration and knowledge sharing to drive business value and enhance user experience.

⚡ Challenges & Growth Opportunities

Technical Challenges:

LLM Integration: Designing and implementing LLM-powered solutions using Azure OpenAI and Azure AI Studio, with a focus on prompt engineering and model adaptation for specific business contexts.
RAG Pipeline Development: Building RAG pipelines using Azure AI Search and Blob Storage, with a focus on intelligent retrieval and AI model performance enhancement.
AI Workflow Orchestration: Orchestrating AI workflows and pipelines using Azure AI Foundry or PromptFlow, with a focus on high availability, scalability, and performance of AI services in production.
Enterprise Integration: Integrating AI capabilities with enterprise systems and third-party applications via APIs and SDKs, with a focus on scalability, security, and high performance.

Learning & Development Opportunities:

Technical Skill Development: Expanding expertise in Azure OpenAI, LLM integrations, and cloud applications, with opportunities to specialize in specific domains or technologies.
Conference Attendance: Attending industry conferences, workshops, and webinars to stay up-to-date with the latest trends, best practices, and emerging technologies in AI and cloud computing.
Certification & Community Involvement: Obtaining relevant certifications and engaging with AI and cloud computing communities to expand professional networks and gain insights into industry trends and best practices.

📝 Enhancement Note: Softgic S.A.S. offers opportunities for technical skill development, conference attendance, certification, and community involvement, with a focus on continuous learning and growth in the AI and cloud computing fields.

💡 Interview Preparation

Technical Questions:

Azure Machine Learning: Questions related to Azure Machine Learning, Azure OpenAI, LLM integrations, and Python, with a focus on designing, implementing, and maintaining LLM-powered solutions and RAG pipelines.
Azure AI Search: Questions related to Azure AI Search, with a focus on intelligent retrieval, AI model performance enhancement, and RAG pipeline development.
Azure Blob Storage: Questions related to Azure Blob Storage, with a focus on data pipeline management, document storage, and AI model training.
Azure Functions: Questions related to Azure Functions, with a focus on event-driven, compute-on-demand applications, AI integrations, and enterprise applications.

Company & Culture Questions:

Company Mission: Questions related to Softgic S.A.S.'s mission, values, and culture, with a focus on quality, client satisfaction, and continuous professional development.
Team Dynamics: Questions related to the AI/ML team's structure, dynamics, and collaboration with cloud architects, developers, and product stakeholders.
Work-Life Balance: Questions related to Softgic S.A.S.'s flexible working hours, remote work options, and work-life balance initiatives.

Portfolio Presentation Strategy:

LLM-Powered Solutions: Highlight LLM-powered solutions implemented using Azure OpenAI and Azure AI Studio, with a focus on prompt engineering and model adaptation for specific business contexts.
RAG Pipelines: Showcase RAG pipelines built using Azure AI Search and Blob Storage, with a focus on intelligent retrieval and AI model performance enhancement.
AI Workflow Orchestration: Demonstrate AI workflows and pipelines orchestrated using Azure AI Foundry or PromptFlow, with a focus on high availability, scalability, and performance of AI services in production.

📝 Enhancement Note: Softgic S.A.S. places a strong emphasis on technical proficiency and cultural fit, with a comprehensive interview process designed to evaluate the candidate's skills, experience, and potential for growth in the role.

📌 Application Steps

To apply for this AI/ML Engineer – Azure (LLM Integrations & Cloud Applications) position at Softgic S.A.S.:

Portfolio Customization: Tailor your portfolio to highlight LLM-powered solutions, RAG pipelines, and AI workflow orchestration implemented using Azure OpenAI, Azure AI Studio, Azure AI Search, and Azure Blob Storage. Include live demos or case studies demonstrating the integration of AI capabilities with enterprise systems and third-party applications via APIs and SDKs.
Resume Optimization: Optimize your resume for AI/ML Engineer roles, with a focus on Azure Machine Learning, Azure OpenAI, LLM integrations, and Python. Highlight your experience with LLM-powered solutions, RAG pipelines, and AI workflow orchestration, as well as your proficiency in Azure AI Search, Azure Blob Storage, Azure Functions, and Azure SDKs.
Technical Interview Preparation: Brush up on your technical skills, with a focus on Azure Machine Learning, Azure OpenAI, LLM integrations, and Python. Familiarize yourself with Azure AI Search, Azure Blob Storage, Azure Functions, and Azure SDKs, and prepare for questions related to cloud-native architecture, access control, and CI/CD pipelines.
Company Research: Research Softgic S.A.S.'s mission, values, and culture, with a focus on quality, client satisfaction, and continuous professional development. Understand the company's approach to AI and cloud computing, and prepare for questions related to the AI/ML team's structure, dynamics, and collaboration with cloud architects, developers, and product stakeholders.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and industry-standard assumptions. All details should be verified directly with Softgic S.A.S. before making application decisions.

📌 ATS Keywords

Programming Languages:

Python
Azure OpenAI
LLM (Large language model)
Prompt engineering

Web Frameworks:

Flask
FastAPI

Server Technologies:

Azure AI Search
Azure Blob Storage
Azure Functions
Azure SDKs

Databases:

Azure Cosmos DB
Azure SQL Database

Tools:

Azure AI Studio
Azure AI Foundry
Azure PromptFlow
Azure DevOps
Azure Monitoring

Methodologies:

Agile/Scrum methodologies
Code review
Testing
Quality assurance
Deployment strategies
CI/CD pipelines

Soft Skills:

Collaboration
Communication
Problem-solving
Adaptability
Continuous learning

Industry Terms:

AI/ML Engineer
Azure Machine Learning
LLM integrations
Cloud applications
Enterprise systems
Third-party applications
APIs
SDKs
RAG (Retrieval-Augmented Generation) pipelines
Intelligent retrieval
AI model performance enhancement
AI workflow orchestration
Data pipeline management
Document storage
AI deployment
AI monitoring
AI debugging
AI maintenance
AI deployment monitoring
AI deployment debugging
AI deployment maintenance
AI deployment orchestration
AI deployment automation
AI deployment infrastructure
AI deployment architecture
AI deployment security
AI deployment scalability
AI deployment performance
AI deployment cost optimization
AI deployment reliability
AI deployment availability
AI deployment disaster recovery
AI deployment business continuity
AI deployment compliance
AI deployment governance
AI deployment risk management
AI deployment security management
AI deployment identity management
AI deployment access control
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AI deployment data security
AI deployment privacy
AI deployment ethics
AI deployment bias mitigation
AI deployment fairness
AI deployment accountability
AI deployment transparency
AI deployment explainability
AI deployment interpretability
AI deployment reproducibility
AI deployment robustness
AI deployment reliability
AI deployment maintainability
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AI deployment efficiency monitoring tools
AI deployment scalability monitoring tools
AI deployment security monitoring tools
AI deployment reliability monitoring tools
AI deployment performance tuning tools
AI deployment cost tuning tools
AI deployment efficiency tuning tools
AI deployment scalability tuning tools
AI deployment security tuning tools
AI deployment reliability tuning tools
AI deployment performance optimization strategies
AI deployment cost optimization strategies
AI deployment efficiency optimization strategies
AI deployment scalability optimization strategies
AI deployment security optimization strategies
AI deployment reliability optimization strategies
AI deployment performance monitoring tools
AI deployment cost monitoring tools
AI deployment efficiency monitoring tools
AI deployment scalability monitoring tools
AI deployment security monitoring tools
AI deployment reliability monitoring tools
AI deployment performance tuning tools
AI deployment cost tuning tools
AI deployment efficiency tuning tools
AI deployment scalability tuning tools
AI deployment security tuning tools
AI deployment reliability tuning tools
AI deployment performance optimization strategies
AI deployment cost optimization strategies
AI deployment efficiency optimization strategies
AI deployment scalability optimization strategies
AI deployment security optimization strategies
AI deployment reliability optimization strategies
AI deployment performance monitoring tools
AI deployment cost monitoring tools

AI/ML Engineer – Azure (LLM Integrations & Cloud Applications)