Machine Learning Eng. (GenAI & Enterprise Systems)

Must to have:

5+ years of software engineering experience, including at least 3 years in ML engineering within production environments.

Strong expertise in Python and machine learning frameworks such as TensorFlow, PyTorch, and Scikit‑learn (or equivalent).

Hands‑on experience deploying machine learning models in enterprise environments.

Experience integrating and operationalizing Large Language Models (LLMs) and Retrieval‑Augmented Generation (RAG) architectures.

Solid understanding of CI/CD practices for ML systems.

Experience working with AWS, Azure, or GCP in enterprise contexts.

Strong knowledge of APIs, distributed systems, and scalable architectures.

Production‑oriented engineering mindset, with ownership over system reliability and scalability.

Adaptability to heterogeneous technology environments across enterprise landscapes.

Strong collaboration and client‑facing capabilities.

Proven enterprise‑level production experience with ML systems.

Hands‑on experience with LLM integrations and RAG architectures.

Strong software engineering fundamentals, including APIs, scalability, testing, and CI/CD.

Real‑world cloud implementation experience.

 

Design and implement end‑to‑end ML and GenAI systems, from development to production deployment.

Develop scalable architectures for RAG‑based applications, LLM integrations, and advanced inference pipelines.

Productionize machine learning models and large language models, ensuring reliability, observability, and maintainability.

Build and optimize data and model pipelines, including training, evaluation, deployment, and monitoring.

Design model‑serving solutions and API integration patterns within enterprise systems.

Optimize model performance for latency, throughput, cost efficiency, and system resilience.

Implement automated testing, evaluation frameworks, and guardrails for ML and LLM systems.

Collaborate with Data Scientists to transition prototypes into production‑grade solutions.

Engage directly with technical teams across industries and technology stacks.

 

Fluent Advanced English 96- 100%

 

Hybrid – México.