Senior AI Engineer, (Mexico-Based, Remote)

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Requirements:

• Demonstrated experience in designing, building, testing, and deploying AI agents using frameworks such as Crew AI, Windsurf, LangChain, Rasa, or equivalent toolkits.
• Proficient in using and integrating AI tools like ChatGPT, OCR technologies, and workflow automation frameworks.
• Proven experience in model development, transfer learning, ensemble methods, and online learning strategies.
• Familiarity with MLOps practices for model monitoring and retraining workflows.
• Proficiency in data preprocessing, handling structured and unstructured data, and managing ETL pipelines for AI applications.
• Hands-on experience deploying AI applications on Azure or AWS, with a strong understanding of cloud-native services and scalability best practices.
• Familiarity with CI/CD pipelines, version control systems, and infrastructure-as-code tools to support seamless deployment of AI solutions.
• Demonstrated ability to analyze complex business or technical processes and develop impactful AI-driven solutions.
• Strong interpersonal and written communication skills, capable of explaining technical solutions to non-technical stakeholders.

Responsibilities:

• Design and implement AI-driven systems, including automating workflows such as data scraping, OCR scanning, PDF processing, and care plan generation.
• Evaluate and apply the best-fit model strategies—training from scratch, fine-tuning pre-trained models, leveraging ensemble learning, or using cloud-based LLMs (e.g., GPT, Claude, Gemini).
• Design, implement, and test AI agents capable of task automation, decision support, and multi-step reasoning using tools such as LangChain, Rasa, Crew AI, and Windsurf.
• Improve models through online learning, user feedback loops, and retraining pipelines based on real-world outcomes.
• Work directly with stakeholders to gather requirements, propose AI solutions, and communicate technical strategies clearly.
• Use advanced tools like ChatGPT, Claude, and open-source LLMs to build smart assistants, workflows, and agents that solve real-world problems.
• Deploy AI applications in cloud environments (AWS, Azure, or GCP) with a focus on scalability, performance, and production readiness.
• Clean and curate datasets for AI consumption, focusing on enhancing quality, structure, and annotation standards.
• Continuously identify bottlenecks and propose AI-powered optimizations to improve processes and reduce manual workloads.
• Architect and optimize agentic pipelines using tools like Crew AI or LangGraph, integrating reasoning chains and tool-use.