GenAI Engineer- R&D and Innovation Center
Posted Jun 28 · 0 applicants
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The role in plain words
- Minimum of 5 years of experience as a Back End developer
- Deep knowledge of Generative AI principles, including Large Language Models (LLMs) and RAG techniques and, modern deployment architectures
- Ability to lead team to design services, modules, APIs, and folder structures that enable REST API communication and facilitate a plug-and-play approach for various agent pods
- Proficiency with Python, PySpark, Langchain, LlamaIndex, RAG, ChromaDB, QDrant, Weaviate, Neo4j, Knowledge Graphs, AWS Bedrock, GCP Vertex, Azure OpenAI, Git/GitHub, Docker, SQL, FastAPI, PyTorch/TensorFlow, and Pydantic
- Experience with managed cloud services provided by hyperscalers such as AWS, Azure, or GCP
- B.A/M.Sc. in Data Science, Computer Science, Mathematics, Statistics, etc
Extracted from the job description · kept up to date automatically
Who this suits
Full job description
Original listing · kept for referenceWe are seeking a talented GenAI Enigneer with a strong background in Large Language Models (LLM)
This is a unique opportunity to work with Deloitte US clients and engage in global projects, leveraging your extensive skills in generative AI tools.
As a pivotal member of our team, you will be responsible for translating complex business problems into data-driven solutions, drafting methodologies, and executing innovative projects.
In this role you will:
• Lead development of robust, efficient, and scalable Python code, ensuring adherence to best practices in software engineering and optimization for high-performance applications.
• Utilize GenAI engineering tools such as LangChain, RAG, Agents, and Vector Database for implementation and optimization.
• Stay updated with AI protocols and standards and ensure their correct application in projects.
• Collaborate with other teams and team members to understand requirements and develop solutions.
• Analyze complex problems, identify and define the computational requirements to solve them.
Requirements:
• Minimum of 5 years of experience as a Back End developer.
• Deep knowledge of Generative AI principles, including Large Language Models (LLMs) and RAG techniques and, modern deployment architectures.
• Ability to lead team to design services, modules, APIs, and folder structures that enable REST API communication and facilitate a plug-and-play approach for various agent pods (compatible with langgraph, pydanticAI, etc.)
• Practical Implementation: Hands-on experience in implementing and integrating new ideas and tools within the GenAI landscape.
• Programming and Tools: Proficiency with Python, PySpark, Langchain, LlamaIndex, RAG, ChromaDB, QDrant, Weaviate, Neo4j, Knowledge Graphs, AWS Bedrock, GCP Vertex, Azure OpenAI, Git/GitHub, Docker, SQL, FastAPI, PyTorch/TensorFlow, and Pydantic.
• Cloud Platforms: Experience with managed cloud services provided by hyperscalers such as AWS, Azure, or GCP.
• Excellent problem-solving and analytical skills. • Degree in Computer Science, Artificial Intelligence, Machine Learning or a related field.
• Advanced level of English.
• Working Israeli permit- a must
• B.A/M.Sc. in Data Science, Computer Science, Mathematics, Statistics, etc- an advantage
Full time Job
Location: Tel Aviv, Hybrid
We at Deloitte believe that diversity and inclusion among our people is a critical component of our success and that is why we cultivate an organizational culture that contains and embraces diversity in all its forms.
Questions about this role
- This listing did not state a salary. We only show pay when the employer publishes it.
- Minimum of 5 years of experience as a Back End developer, Deep knowledge of Generative AI principles, including Large Language Models (LLMs) and RAG techniques and, modern deployment architectures, Ability to lead team to design services, modules, APIs, and folder structures that enable REST API communication and facilitate a plug-and-play approach for various agent pods, Proficiency with Python, PySpark, Langchain, LlamaIndex, RAG, ChromaDB, QDrant, Weaviate, Neo4j, Knowledge Graphs, AWS Bedrock, GCP Vertex, Azure OpenAI, Git/GitHub, Docker, SQL, FastAPI, PyTorch/TensorFlow, and Pydantic, Experience with managed cloud services provided by hyperscalers such as AWS, Azure, or GCP