Data Scientist – Machine Learning & AI – ID #00361

Location: LATAM
Employment Type: Full-time

We are looking for a Senior AI Engineer with strong experience building production-grade LLM applications, Machine Learning solutions, and Agentic AI systems.

The ideal candidate combines strong Python software engineering skills with hands-on experience in Machine Learning, LLMs, embeddings, NLP, and AI application development. You will work on conversational experiences and intelligent workflows, leveraging modern LLM technologies, tool calling, context management, and multi-step orchestration.

This role is suited for an engineer who enjoys solving complex AI challenges and continuously improving production systems through experimentation, evaluation, performance optimization, and engineering best practices.

You will collaborate closely with software engineers and AI specialists to design and deliver scalable, reliable, and production-ready AI capabilities.

Requirements

• 5+ years of professional experience in Data Science, Machine Learning, AI Engineering, or a related field.
• Strong experience building and maintaining production AI and Machine Learning applications.
• Strong proficiency in Python and modern software engineering best practices.
• Strong SQL skills and experience working with cloud data warehouses.
• Strong practical knowledge of Machine Learning, LLMs, embeddings, and Natural Language Processing (NLP).
• Hands-on experience building production-grade LLM and Agentic AI applications.
• Experience building, evaluating, and maintaining production Machine Learning models.
• Hands-on experience with TensorFlow, PyTorch, PyCaret, or similar Machine Learning frameworks.
• Experience implementing tool calling, MCP (Model Context Protocol) servers, context management, and multi-step orchestration.
• Experience integrating hosted LLM APIs such as OpenAI and Anthropic into production environments.
• Strong experience with prompt engineering, prompt versioning, fallback strategies, and LLM cost and latency optimization.
• Experience designing intent classification and routing solutions for natural language applications.
• Experience building conversational AI solutions or multi-API orchestration workflows.
• Experience developing LLM and Machine Learning evaluation methodologies and frameworks.
• Ability to work effectively within existing codebases and production architectures.
• Strong understanding of AI/ML scalability, inference performance, reliability, and production engineering principles.
• Strong collaboration, communication, analytical, and problem-solving skills.
• Upper-Intermediate to Advanced English proficiency (B2+/C1).
• Availability to work remotely with reasonable overlap with business hours.

Nice to Have

• Experience deploying, fine-tuning, and evaluating open-source models using Hugging Face.
• Experience with GPU acceleration and inference optimization.
• Experience implementing MLOps practices, including CI/CD, observability, and reproducible pipelines.
• Experience with vector databases and Retrieval-Augmented Generation (RAG).
• Advanced or applied experience with Natural Language Processing (NLP).
• Experience building client-facing AI products.
• Experience working with embeddings, semantic search, or similarity-based applications.
• AWS certification or equivalent cloud expertise.

Responsibilities

• Design, build, and maintain MCP servers, tool definitions, and context management capabilities.
• Develop and maintain AI-powered workflows using LLMs, tool calling, and multi-step orchestration.
• Design and implement intent classification, evaluation, and routing workflows.
• Integrate OpenAI, Anthropic, and other hosted LLM APIs into production applications.
• Develop and maintain effective prompt engineering and prompt versioning strategies.
• Implement fallback mechanisms and optimize LLM performance, latency, reliability, and cost.
• Build, evaluate, maintain, and improve Machine Learning models in production environments.
• Apply Machine Learning, embeddings, NLP, and LLM techniques to solve complex business and technical problems.
• Design and maintain LLM and Machine Learning evaluation frameworks to measure model quality and production performance.
• Analyze AI/ML system behavior, identify edge cases, and continuously improve model and application performance.
• Collaborate closely with software engineers, data professionals, and AI specialists to deliver production-ready solutions.
• Participate in architecture discussions and contribute to the design and evolution of AI platforms.
• Work within existing codebases and architectures while identifying opportunities for technical improvement.
• Continuously improve AI systems through experimentation, evaluation, optimization, and engineering best practices.
• Help ensure AI solutions are scalable, maintainable, observable, reliable, and suitable for production environments.

What Sets You Apart

• Strong hands-on experience building LLM-powered applications and Machine Learning solutions in production.
• Practical experience designing and deploying Agentic AI or conversational AI solutions.
• Strong understanding of Machine Learning, embeddings, NLP, LLMs, and AI application development.
• Deep understanding of prompt engineering, LLM integrations, tool calling, MCP, and evaluation methodologies.
• Strong software engineering mindset applied to AI and Machine Learning systems.
• Ability to balance AI quality, reliability, latency, scalability, and cost.
• Comfortable working with ambiguous and complex technical challenges.
• Strong autonomy and ownership.
• Excellent communication skills and the ability to explain technical concepts to different stakeholders.
• Upper-Intermediate to Advanced English proficiency (B2+/C1).

What We Offer

• Fully remote work environment.
• Opportunity to work on cutting-edge Generative AI, LLM, Machine Learning, and Agentic AI initiatives.
• Exposure to modern technologies including MCP, RAG, vector databases, embeddings, NLP, and AI orchestration.
• Collaboration with distributed and international technical teams.
• An environment focused on experimentation, innovation, and technical ownership.
• Opportunity to design and build production-grade AI solutions with real-world impact.