Internally, we’re forming a dedicated tech team for a special project connected to our service operation in the banking industry. At this stage, we can’t disclose full details, but it involves a critical initiative where reliability, security, and seamless system integration are essential. The main challenge is to build stable, scalable features that align with the strict standards of financial operations and work smoothly within our current ecosystem. The position exists because this new initiative requires strong technical ownership from day one.
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Design, train, and deploy Machine Learning models and LLM-based systems for production use cases. Build and optimize end-to-end ML pipelines including preprocessing, training, evaluation, experimentation, and model lifecycle management. Develop Retrieval-Augmented Generation (RAG) pipelines and embedding-based search systems. Perform fine-tuning of LLMs and implement model versioning, monitoring, and AB testing. Create AI-driven services for features such as: Analytical and conversational assistants; Intelligent receipt or document generation; Predictive insights and recommendations. Implement high standards for observability, testability, monitoring, and metrics across all ML and AI components. Collaborate closely with backend and full-stack engineers to integrate models into scalable production services. Communicate technical decisions clearly to both technical and non-technical stakeholders.
Core Experience: 5+ years of hands-on experience building and deploying Machine Learning systems in production. Proven experience implementing LLMs, including: embeddings, vector stores, RAG pipelines, fine-tuning, prompting, and LLM evaluation. Demonstrated experience building production-grade AI systems, not just prototypes or PoCs. Strong knowledge of neural networks, inference optimization, and model performance evaluation. Technical Skills: Python for ML/AI development; experience with ML frameworks such as PyTorch or TensorFlow; familiarity with LLM tooling (e.g., HuggingFace, LangChain, LlamaIndex). Experience with cloud infrastructure (AWS, GCP, or Azure). Hands-on experience with model observability, logging, metrics, and monitoring (MLflow, Prometheus, OpenTelemetry, etc.). Experience with APIs, microservices, containers, and deployment tools (Docker, Kubernetes). Ability to integrate or collaborate with services built in Golang (nice to have, not required). Soft Skills: strong ownership and ability to operate autonomously; excellent English communication; able to work with international teams; comfortable in fast-paced, evolving environments; collaborative, team-first mindset; hands-on, technical operator mentality to build and run systems end-to-end. Nice to Have: experience with LLM optimization or cost-efficient inference; analytics or data-intensive product experience; familiarity with TypeScript or React; MLOps or data engineering background.
Experience optimizing LLM workloads for cost and latency; track record of deploying analytics-driven products; exposure to front-end collaboration; familiarity with modern frontend tools (TypeScript, React) to better align with full-stack engineering efforts; deeper experience in MLOps workflows and data engineering patterns.
Hybrid work arrangement with monthly in-office collaboration in Mexico City (1-2 days).
GETONBRD Job ID: 57702
Hybrid
This job is performed partly from home and partly at the office in Ciudad de México (Mexico).
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