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במקום לעבור לבד על אלפי מודעות, Jobify מנתחת את קורות החיים שלך ומציגה לך רק משרות שבאמת מתאימות לך.
מעל 80,000 משרות • 4,000 חדשות ביום
חינם. בלי פרסומות. בלי אותיות קטנות.
מלאה
30,000-45,000 ₪
The role offers significant business impact, transforming complex challenges into scalable AI-powered products and solutions used in large-scale production environments. Key Responsibilities: Lead the end-to-end architecture, design, development, and deployment of advanced Generative AI applications, including Multi-Agent Systems and Retrieval-Augmented Generation (RAG) solutions. Design and optimize RAG pipelines, integrating Large Language Models (LLMs) with structured and unstructured data, including Knowledge Graphs and Vector Stores. Build, manage, and automate Generative AI solutions using AWS services, including Bedrock, S3, SageMaker, Lambda, and Step Functions. Lead Proof of Concepts (POCs) and evaluate emerging GenAI technologies and products to identify the most suitable solutions for business needs. Integrate AI solutions with existing enterprise systems using APIs and Microservices. Collaborate with data Engineers, Analysts, and Product Managers to translate business requirements into scalable and practical AI solutions. Implement advanced MLOps processes, including CI/CD, Docker, and monitoring solutions, to ensure the reliability, scalability, and maintainability of AI applications.
Requirements:
Requirements 5+ years of hands-on Python development experience, with expertise in building and deploying AI and Machine Learning applications - mandatory. Extensive hands-on experience with AWS services relevant to AI/ML, including S3, Glue, Athena, SageMaker, Lambda, and Bedrock - mandatory. Proven experience with Generative AI frameworks such as LangChain, LlamaIndex, or Haystack - mandatory. Hands-on experience designing and deploying RAG-based applications, along with practical knowledge of Vector Databases such as Pinecone, Weaviate, or ChromaDB and document indexing techniques - mandatory. Strong understanding of software development principles, including Git version control, clean code practices, and Unit Testing - mandatory. Strong analytical and problem-solving skills, with the ability to break down complex business challenges into actionable technical solutions - mandatory. Excellent communication and collaboration skills, with the ability to clearly explain technical concepts to both technical and non-technical stakeholders - mandatory. Nice to Have: Domain Knowledge: Previous experience in the financial services, fintech, or a related highly-regulated industry. Database Experience: Experience with specialized databases such as graph databases (e.g., Neo4j, Amazon Neptune). MLOps Tools: Familiarity with MLOps platforms beyond AWS, such as Kubeflow or MLflow. Education: A Master's or Ph.D. in Computer Science, AI, or a related field.
Requirements 5+ years of hands-on Python development experience, with expertise in building and deploying AI and Machine Learning applications - mandatory. Extensive hands-on experience with AWS services relevant to AI/ML, including S3, Glue, Athena, SageMaker, Lambda, and Bedrock - mandatory. Proven experience with Generative AI frameworks such as LangChain, LlamaIndex, or Haystack - mandatory. Hands-on experience designing and deploying RAG-based applications, along with practical knowledge of Vector Databases such as Pinecone, Weaviate, or ChromaDB and document indexing techniques - mandatory. Strong understanding of software development principles, including Git version control, clean code practices, and Unit Testing - mandatory. Strong analytical and problem-solving skills, with the ability to break down complex business challenges into actionable technical solutions - mandatory. Excellent communication and collaboration skills, with the ability to clearly explain technical concepts to both technical and non-technical stakeholders - mandatory. Nice to Have: Domain Knowledge: Previous experience in the financial services, fintech, or a related highly-regulated industry. Database Experience: Experience with specialized databases such as graph databases (e.g., Neo4j, Amazon Neptune). MLOps Tools: Familiarity with MLOps platforms beyond AWS, such as Kubeflow or MLflow. Education: A Master's or Ph.D. in Computer Science, AI, or a related field.
This position is open to all candidates.
במקום לעבור לבד על אלפי מודעות, Jobify מנתחת את קורות החיים שלך ומציגה לך רק משרות שבאמת מתאימות לך.
מעל 80,000 משרות • 4,000 חדשות ביום
חינם. בלי פרסומות. בלי אותיות קטנות.
תל אביב - יפו
ערב
7 חודשים
