לוח משרות דרושים Gen AI Engineer בתל אביב יפו

We are seeking a talented Software Development Leader with a strong background in Large Language Models (LLM) and GenAI engineering.
This is a unique opportunity to work with 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.

Together with our customers, were helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.

Title and Summary
Product Manager - Generative AI (Prompt)
Dynamic Yield, were on a mission to shape the future of personalization. As a global leader in experience optimization, we help hundreds of brands deliver tailored content and product recommendations to millions of users in real time.

We are now hiring our first Product Manager to lead the development of GenAI-powered experiences. Youll work across multiple product lines to design and implement production-grade prompt architectures, embed AI agents into the user journey, and scale experimentation with cutting-edge LLM tools.

Product Team Member Prompt Engineer

This role blends hands-on AI development with product integration strategy. Youll collaborate with product managers and engineers to design, evaluate, and iterate on generative AI solutions including multi-step prompt chains, agent workflows, and LLM selection frameworks. Your work will shape how personalization evolves across the Dynamic Yield platform.

All About the Role
Bring experience delivering products in SaaS startups or enterprise environments, with a deep understanding of market dynamics, customer needs, and the competitive landscape.
Design and refine context and prompt architectures (e.g., few-shot, chain-of-thought, tool use) that drive our LLM-powered features
Select and evaluate models, frameworks, and orchestration tools (e.g., OpenAI, Claude, LangChain, RAG)
As part of the product team you will collaborate cross-functionally with developers, UX, BI, and Product Marketing to ensure product success across the board
Collaborate with engineers to implement scalable agent-based workflows and integrate GenAI into the product
Partner with Product Managers to define user-facing use cases and influence product direction with AI capabilities
Develop internal tooling and evaluation frameworks to test prompt quality, model behavior, and UX alignment
Act as a thought leader on prompt best practices, model safety, and responsible AI design across product squads

Were looking for an experienced MLOps / DevOps Engineer to design and manage the infrastructure powering large-scale machine learning systems. Youll be responsible for deploying GPU-heavy models (including LLMs) on cost-efficient, production-grade infrastructure, supporting both ML workflows and application artifact delivery.

Youll work with cutting-edge technologies like vLLM, Triton, SageMaker, ClearML, Karpenter, KEDA, and EKS, ensuring the right balance between performance, scalability, and cost.



What Youll Do

Deploy and manage LLMs and deep learning models using vLLM, Triton Inference Server, and custom API endpoints.

Build and maintain GPU-aware autoscaling clusters using AWS EKS, Karpenter, and KEDA, optimizing for cost-efficiency and performance.

Develop CI/CD pipelines using Jenkins and GitHub Actions to automate ML model delivery and application deployments.

Orchestrate training, fine-tuning, and inference jobs on AWS SageMaker and ClearML, with support for experiment tracking, versioning, and reproducibility.

Support backend teams in deploying app artifacts and runtime environments; implement rollback and release strategies.

Integrate observability tooling (e.g., Prometheus, Grafana, ELK, or OpenTelemetry) for both infrastructure and model performance.

Collaborate with SREs to enforce high availability, disaster recovery, and incident response procedures for mission-critical AI services.

We are now hiring our first Prompt Engineer to join the Product team and lead the development of GenAI-powered experiences. Youll work across multiple product lines to design and implement production-grade prompt architectures, embed AI agents into the user journey, and scale experimentation with cutting-edge LLM tools.

Product Team Member Prompt Engineer

This role blends hands-on AI development with product integration strategy. Youll collaborate with product managers and engineers to design, evaluate, and iterate on generative AI solutions including multi-step prompt chains, agent workflows, and LLM selection frameworks. Your work will shape how personalization evolves across the Dynamic Yield platform.

All About the Role

Design and refine prompt architectures (e.g., few-shot, chain-of-thought, tool use) that drive our LLM-powered features

Select and evaluate models, frameworks, and orchestration tools (e.g., OpenAI, Claude, LangChain, RAG)

Collaborate with engineers to implement scalable agent-based workflows and integrate GenAI into the product

Partner with Product Managers to define user-facing use cases and influence product direction with AI capabilities

Develop internal tooling and evaluation frameworks to test prompt quality, model behavior, and UX alignment

Act as a thought leader on prompt best practices, model safety, and responsible AI design across product squads

Were looking for a hands-on AI Architect to join our companys Platform infrastructure (Gen AI applications team) where you'll have the unique opportunity to collaborate with our companys strategic technical leadership and the development of next-generation technologies for modern software release management, driving the industry forward.
As an AI Architect at our company you will.
Design, develop, and implement various ML and AI-based solutions to address business needs and objectives
Collaborate with key organizational stakeholders to understand AI requirements and design end-to-end AI solutions
Explore and experiment with novel ML and AI techniques and architectures to drive innovation
Evaluate and recommend ML and AI tools and frameworks to enhance productivity and effectiveness
Ensure the scalability and reliability of LLM-based system architectures
Provide technical guidance and mentorship to development teams on AI and ML technologies and practices
Influence the AI priorities of the company. Your decisions will shape our future direction in AI, and have a significant impact on our companys AI initiatives.

We are seeking for an experienced, hands-on Senior ML / AI Engineer to join the Generative AI applications platform group at our company and lead the implementation and integration of AI/ML solutions.
As a Senior ML / AI Engineer at our company you will...
Understand the challenges our users face and how to integrate and implement AI/ML solutions to solve those challenges
Work with stakeholders such as engineers, Product Management, and analysts to translate business problems to technical requirements and solutions
Design, develop, and implement POCs for ML models and GenAI solutions to address business needs and objectives
Provide technical guidance and mentorship to development teams on AI/ML technologies and practices
Build ML capabilities from ideation to production.

We are responsible for the research and development of advanced experimentation and Causal Analysis infrastructures to measure, understand and attribute features impact. We also own a comprehensive Data Warehouse, providing essential dashboards and analytics tools, including Gen-AI agents, to empower both Data Scientists and stakeholders throughout the organization.

Responsibilities
Develop and implement Measurement Methodologies and Evaluation Frameworks. Assess the performance, accuracy and groundedness of AI Agents, including the automated generation of synthetic training and evaluation data.
Work with various teams including software developers, data engineers and product managers, to define the requirements and implement Gen-AI models and solutions to drive innovation.
Translate complex technical concepts and investigative findings into clear, actionable insights for both technical and non-technical stakeholders.
Lead the design, development and deployment of Generative AI Agents for conversational driving-related experiences and scaling the ingestion, annotation and utilization of user generated contect and geospatial data feeds.

We are looking for a Senior Software Engineer to join our growing Innovative DataEngine team!
This is a great opportunity to be part of one of the fastest-growing infrastructure companies in history. This organization is in the center of the hurricane created by the artificial intelligence revolution.
As a Data Pipelines Engineer,
You will be building pipelines on top of the Insight Engine, a one-stop shop to build fully integrated AI pipelines. You will build new and innovative AI pipelines and applications as blueprints for customers and AI practitioners to use.
We are the data platform company for the AI era. We are building the enterprise software infrastructure to capture, catalog, refine, enrich, and protect massive datasets and make them available for real-time data analysis and AI training and inference. Designed from the ground up to make AI simple to deploy and manage, we take the cost and complexity out of deploying enterprise and AI infrastructure across data centers, edge, and cloud.

As a Big Data & GenAI Engineering Lead within our company's Data & AI Department, you will play a pivotal role in building the data and AI backbone that empowers product innovation and intelligent business decisions. You will lead the design and implementation of our companys next-generation lakehouse architecture, real-time data infrastructure, and GenAI-enriched solutions, helping drive automation, insights, and personalization at scale. In this role, you will architect and optimize our modern data platform while also integrating and operationalizing Generative AI models to support go-to-market use cases. This includes embedding LLMs and vector search into core data workflows, establishing secure and scalable RAG pipelines, and partnering cross-functionally to deliver impactful AI applications.
As a Big Data & GenAI Engineering Lead in our company you will...
Design, lead, and evolve our companys petabyte-scale Lakehouse and modern data platform to meet performance, scalability, privacy, and extensibility goals.
Architect and implement GenAI-powered data solutions, including retrieval-augmented generation (RAG), semantic search, and LLM orchestration frameworks tailored to business and developer use cases.
Partner with product, engineering, and business stakeholders to identify and develop AI-first use cases, such as intelligent assistants, code insights, anomaly detection, and generative reporting.
Integrate open-source and commercial LLMs securely into data products using frameworks such as LangChain, or similar, to augment AI capabilities into data products.
Collaborate closely with engineering teams to drive instrumentation, telemetry capture, and high-quality data pipelines that feed both analytics and GenAI applications.
Provide technical leadership and mentorship to a cross-functional team of data and ML engineers, ensuring adherence to best practices in data and AI engineering.
Lead tool evaluation, architectural PoCs, and decisions on foundational AI/ML tooling (e.g., vector databases, feature stores, orchestration platforms).
Foster platform adoption through enablement resources, shared assets, and developer-facing APIs and SDKs for accessing GenAI capabilities.