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Senior Generative AI Engineer
PresencialTiempo completoAI EngineeringBanking
Lead end-to-end development and deployment of generative AI solutions, translating business needs into scalable applications and guiding their adoption across the organization. This full-time, on-site role is based in Lod.
Responsabilidades
- Set the strategy for generative AI solutions and lead their development, deployment, measurement, and adoption.
- Design, develop, and deploy generative AI applications, including multi-agent systems and RAG solutions.
- Build and optimize RAG pipelines integrating language models with structured and unstructured data, knowledge graphs, and vector stores.
- Use AWS services to build, manage, and automate generative AI solutions.
- Lead proof-of-concept work and evaluate emerging generative AI products and technologies.
- Integrate AI solutions with existing banking systems through APIs and microservices.
- Collaborate with data engineers, analysts, and product managers to translate business needs into practical AI solutions.
- Implement MLOps processes, including CI/CD, Docker, and monitoring, to support reliable and maintainable applications.
Requisitos
- At least 7 years of hands-on Python or TypeScript development, including at least 2 recent years building and deploying AI or machine-learning applications.
- Extensive practical experience with AWS services relevant to AI and machine learning, including S3, Glue, Athena, Lambda, and Bedrock.
- Experience with generative AI frameworks such as LangChain, LlamaIndex, or Haystack.
- Hands-on experience designing and deploying RAG applications, using vector databases, and applying document indexing techniques.
- Strong software development fundamentals, including Git, design, disaster recovery, clean code, and unit and end-to-end testing.
- Product-level understanding of software architecture.
- Strong problem-solving skills and ability to turn complex business challenges into practical technical work.
Se valora
- Experience in financial services, fintech, or regulated industries.
- Familiarity with graph databases such as Neo4j or Amazon Neptune.
- Experience with MLOps platforms beyond AWS, such as Kubeflow or MLflow.
- A master's degree or doctorate in computer science, artificial intelligence, or a related field.