Lead Machine Learning Engineering

Poland and Middle-Eastern Europe; Colombia; Presencial; COP 65.000.000 - 120.000.000; Jornada completa

Descripción de la vacante

Senior AI Engineer role focusing on designing, building, and deploying production-ready AI solutions using modern LLMs and AI agents. You will implement RAG workflows, vector databases, and semantic search while integrating leading platforms like Azure OpenAI, AWS Bedrock, or Vertex AI.

You’ll develop backend services and APIs with Python and FastAPI, collaborate with frontend teams (React), and drive prompt engineering, testing, and reliable cloud deployments using Terraform and CI/CD pipelines.

Formación

• 10+ years of software engineering experience with recent hands-on Generative AI work.
• Strong Python and REST API skills.
• Experience building production AI apps using LLMs.
• Hands-on experience with AI agent frameworks (LangChain, CrewAI).
• Experience implementing Retrieval-Augmented Generation (RAG) architectures.
• Experience integrating AI platforms like Azure OpenAI, AWS Bedrock, Google Vertex AI, or equivalent.
• Strong prompt engineering and API design skills.
• Experience designing scalable cloud applications on AWS.
• Familiarity with Docker, Terraform, CI/CD, and IaC.
• Experience with SQL and NoSQL databases.
• Familiarity with React or modern frontend tech.
• Experience working in Agile teams.
• Strong understanding of software architecture and distributed systems.

Responsabilidades

• Design, develop, and maintain AI-powered applications using LLMs and Generative AI.
• Build AI agents and RAG solutions for knowledge-based apps.
• Integrate AI platforms (Azure OpenAI, AWS Bedrock, Vertex AI).
• Develop scalable backend services and APIs with Python and FastAPI.
• Collaborate with frontend engineers to deliver end-to-end AI apps (React).
• Design prompt engineering strategies to improve model performance.
• Implement intelligence routing, semantic search, vector databases, and knowledge retrieval.
• Deploy and manage cloud-native AI apps using AWS and Terraform.
• Build CI/CD pipelines, containerized apps, and IaC best practices.
• Evaluate new AI frameworks and models to improve platform capabilities.
• Collaborate with Product/Architect/Engineering to translate requirements.
• Mentor engineers and contribute to technical leadership.

Conocimientos

Python

REST APIs

LLMs

AI/ML

RAG

AWS

Docker

Terraform

FastAPI

React

SQL/NoSQL

Distributed systems

Agile

Herramientas

LangChain

CrewAI

Docker

Terraform

FastAPI

AWS

React

Descripción del empleo

Role Overview

We are looking for a Senior AI Engineer who can design, build, and deploy production-ready AI solutions using modern Large Language Models (LLMs), AI agents, and cloud-native architectures. The ideal candidate combines strong software engineering fundamentals with hands‑on experience building scalable AI applications, integrating foundation models, and delivering business value through Generative AI.

Key Responsibilities

• Design, develop, and maintain AI-powered applications using Large Language Models
  (LLMs) and Generative AI technologies
• Build AI agents and Retrieval-Augmented Generation (RAG) solutions to enable intelligent workflows and knowledge-based applications.
• Integrate leading AI platforms such as Azure OpenAI, Amazon Bedrock, Google Vertex AI, or similar services.
• Develop scalable backend services and APIs using Python and modern frameworks such as FastAPI.
• Collaborate with frontend engineers to deliver end-to-end AI applications using technologies such as React.
• Design prompt engineering strategies to improve model accuracy, reliability, and user experience.
• Implement intelligent routing, semantic search, vector databases, and knowledge retrieval solutions.
• Deploy and manage cloud-native AI applications using AWS and Infrastructure as Code tools such as Terraform.
Build and maintain CI/CD pipelines, containerized applications, and cloud infrastructure using Docker and DevOps best practices.
• Evaluate emerging AI frameworks, tools, and models to continuously improve platform capabilities.
• Collaborate with Product Managers, Architects, and Engineering teams to translate business requirements into scalable AI solutions.
• Mentor engineers and contribute to technical leadership, architecture discussions, and engineering best practices.

Required Qualifications

• 10+ years of experience in Software Engineering with recent hands‑on experience building Generative AI solutions.
• Strong experience with Python and REST API development.
• Experience developing production AI applications using Large Language Models (LLMs).
• Hands‑on experience with AI agent frameworks such as LangChain, CrewAI, or similar technologies.
• Experience implementing Retrieval-Augmented Generation (RAG) architectures.
• Experience integrating AI platforms such as Azure OpenAI, Amazon Bedrock, Google Vertex AI, or equivalent services.
• Strong understanding of prompt engineering techniques and AI application design patterns.
• Experience developing scalable cloud applications on AWS.
• Experience with Docker, Terraform, CI/CD pipelines, and Infrastructure as Code.
• Experience with SQL and NoSQL databases.
• Familiarity with React or modern frontend technologies.
• Experience working within Agile software development environments.
• Strong understanding of software architecture, API design, and distributed systems.
• Experience working in cross‑functional and multicultural teams.

Working Style

• Strong communication skills: able to clearly explain complex AI concepts to both technical and non-technical audiences.
• Proactive mindset: identifies opportunities for innovation and continuously explores new AI technologies.
• Ownership and accountability: takes responsibility for delivering reliable, scalable, and maintainable AI solutions.
• Collaborative attitude: works effectively across product, engineering, architecture, and business teams.
• Adaptability: thrives in a rapidly evolving AI landscape and embraces continuous learning.
• Attention to detail: prioritizes quality, security, observability, and responsible AI practices.
• Customer-oriented thinking: focuses on solving real business problems through practical AI solutions.
• Continuous learner: stays current with advancements in LLMs, AI frameworks, cloud services, and software engineering best practices