Senior Data Scientist

Finastra

Guadalajara, Mexico

#10752

Position summary

nsibilities & deliverables:**

  • Design, develop, and deploy production-grade large language model systems across a wide variety of applications (e.g. RAG, text-to-SQL, specialized agents etc., summarization, etc.)

  • Architect and implement agent and multi-agent systems leveraging state-of-the-art LLM frameworks

  • Creation of tools and functions to enhance LLM capability

  • Establish and maintain model lifecycle management practices, including version control, evaluation metrics, and performance monitoring

  • Optimize LLM applications for production environments, balancing performance, cost, and latency requirements

  • Perform robust model validation and security assessments of LLM systems

  • Collaborate closely with product teams, data engineers, and software engineers to integrate LLM capabilities into products and services

  • Lead technical discussions and present complex AI concepts to both technical and non-technical stakeholders

  • Stay current with the rapidly evolving LLM landscape and implement best practices

  • Mentor junior data scientists and engineers across the organization in LLM development techniques

Required skills & experience:

  • 5+ years of professional experience in data science or machine learning engineering

  • Proven experience developing and deploying production-grade large language model systems

  • Strong proficiency in Python and related data science/ML libraries

  • Experience with LLM frameworks such as LangGraph or similar orchestration tools

  • Practical knowledge of prompt engineering, fine-tuning, retrieval-augmented generation, text-to-sql, and other related LLM tasks

  • Experience implementing agent and multi-agent systems with LLMs

  • Strong knowledge of deploying LLM systems and agents in production environments

  • Strong understanding of model lifecycle management and monitoring practices

  • Familiarity with cloud environments (e.g. Azure, databricks) and MLOps best practices

  • Experience with distributed computing and handling large-scale data processing

  • Thorough understanding of security best practices around LLM systems

  • Excellent collaboration skills and experience working with cross-functional teams

  • Strong knowledge of software development best practices (version control, CI/CD, testing)

  • Exceptional leadership, communication, and presentation skills

  • Advanced degree (MS or PhD) in Computer Science, Machine Learning, or related technical field

Technical Skills:

Aside from Python and SQL, we do not require previous working experience with most of the other skills listed below.

  • Programming Languages: Python (required), SQL

  • ML/LLM Frameworks: LangChain, LangGraph, Hugging Face Transformers, PyTorch, TensorFlow, or equivalent

  • Vector Databases: Pinecone, Weaviate, Milvus, Qdrant, or similar

  • Cloud Platforms: Azure, Databricks, AWS, or equivalent

  • MLOps Tools: MLflow, Weights & Biases, Kubeflow, or similar

  • Development Tools: Git, GitHub/GitLab, CI/CD pipelines

  • Data Processing: Pandas, PySpark, Dask, Spark, or equivalent

  • Containerization: Docker, Kubernetes

  • API Development: FastAPI, Flask, Langgraph Platform

  • Monitoring & Observability: Langsmith, Prometheus, Grafana, or similar

#LI-MG1