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Senior Generative AI Engineer
·Israel
Not specifiedFull-timeAI Engineering
Lead the architecture and development of cutting-edge Generative AI systems, integrating LLMs with RAG, vector stores, and knowledge graphs. Drive Proof of Concepts, automate workflows with AWS services, and implement MLOps practices for scalable AI applications.
Responsibilities
- Lead end-to-end architecture, design, and deployment of advanced Generative AI applications, including multi-agent systems and RAG solutions.
- Design and optimize RAG pipelines, integrating LLMs with structured and unstructured data via knowledge graphs and vector stores.
- Build, manage, and automate GenAI solutions using AWS services like Bedrock, SageMaker, Lambda, and Step Functions.
- Lead Proof of Concepts and evaluate emerging GenAI technologies to identify optimal solutions for business needs.
- Integrate AI solutions with existing enterprise systems through APIs and microservices.
- Collaborate with Data Engineers, Analysts, and Product Managers to translate requirements into scalable AI solutions.
- Implement advanced MLOps processes, including CI/CD, Docker, and monitoring, to ensure reliability and scalability.
Requirements
- 5+ years of hands-on Python development, with expertise in building and deploying AI/ML applications.
- Extensive hands-on experience with AWS AI/ML services, including S3, Glue, Athena, SageMaker, Lambda, and Bedrock.
- Proven experience with Generative AI frameworks such as LangChain, LlamaIndex, or Haystack.
- Hands-on experience designing and deploying RAG-based applications and using vector databases like Pinecone, Weaviate, or ChromaDB.
- Strong understanding of software development principles, including Git, clean code, and unit testing.
- Strong analytical and problem-solving skills to break down complex business challenges.
- Excellent communication and collaboration skills to explain technical concepts to diverse stakeholders.
Nice to have
- Domain knowledge in financial services, fintech, or a related highly-regulated industry.
- Experience with specialized databases such as graph databases (e.g., Neo4j, Amazon Neptune).
- Familiarity with MLOps platforms beyond AWS, like Kubeflow or MLflow.
- A Master's or Ph.D. in Computer Science, AI, or a related field.