דרושים » הנדסה » Senior Software Engineer (GenAI Platform)

We are seeking an experienced Generative AI Engineer to join our AI squad at our company. This is a unique opportunity to wear multiple hats - serving as both a developer of cutting-edge GenAI solutions and an advisory expert helping organizations transform their AI capabilities. You'll build end-to-end GenAI projects from conception to production while staying at the forefront of this rapidly evolving field.
Key Responsibilities
GenAI Development & Implementation
End-to-End Development: Build GenAI solutions from POC through production deployment, handling all backend development responsibilities
Client Engagement: Participate in technical discussions with clients, gather requirements, and help translate business visions into feasible technical solutions through presentations and consultations
Backend Development: Design and implement production-grade microservices architectures for GenAI applications using Python
Cloud Implementation: Deploy and manage GenAI solutions across GCP, Azure, and AWS platforms, leveraging cloud-native AI services
Cross-functional Collaboration: Work closely with project managers, full-stack developers, and Power Automate teams to deliver complete solutions
System Evaluation: Assess and optimize production-grade GenAI systems for performance, scalability, and reliability
Continuous Learning & Innovation
Technology Scouting: Continuously explore and evaluate new GenAI models, frameworks, and techniques as they emerge
Best Practices Development: Establish and refine methodologies for GenAI solution development and deployment.

We are seeking a Backend Team Lead to spearhead the development of ludeo.ai, our GenAI-powered product that enables users to generate interactive (gaming experiences) directly from prompts or video content. This is a high-impact leadership role at the intersection of backend architecture, multimodal AI, and real-time systems. You will architect and lead the AI engine that transforms unstructured inputs (text/video) into structured, interactive gaming playable moments.

What Youll Do

Lead & Mentor: Build and manage a high-performing backend/AI engineering team, drive architectural decisions, and foster rapid innovation while maintaining production-grade reliability.
Design AI-Native Systems: Architect scalable microservices powering complex AI workflows. Design and implement Retrieval-Augmented Generation (RAG) pipelines, embedding strategies, and vector database infrastructure (e.g., Pinecone, Weaviate, Milvus, PGVector). Optimize retrieval, prompt orchestration, latency, and cost.
Agentic Workflows: Design multi-agent systems using planner/executor/tool-calling patterns. Implement stateful, multi-step AI workflows with frameworks such as LangChain, CrewAI, AutoGen, or similar. Build evaluation, observability, and safety mechanisms for LLM systems.
Multimodal AI: Integrate multimodal models (vision + text) to understand video and translate it into structured form.
Scale & Infrastructure: Ensure robustness, security, and high availability on AWS/Kubernetes. Design distributed systems that handle real-time data and AI workloads efficiently.
Collaborate: Work closely with Product and Design to translate GenAI capabilities into stable, scalable production features.

We are seeking a Backend Engineer to join the development of ludeo.ai, our GenAI-powered product that enables users to generate interactive (gaming experiences) directly from prompts or video content. This is a high-impact role at the intersection of backend architecture, multimodal AI, and real-time systems. You will contribute to the AI engine that transforms unstructured inputs (text/video) into structured, interactive gaming playable moments.

What Youll Do

Design AI-Native Systems: Design and implement scalable microservices powering complex AI workflows. Design and implement Retrieval-Augmented Generation (RAG) pipelines, embedding strategies, and vector database infrastructure (e.g., Pinecone, Weaviate, Milvus, PGVector). Optimize retrieval, prompt orchestration, latency, and cost.
Agentic Workflows: Design and implement multi-agent systems using planner/executor/tool-calling patterns. Implement stateful, multi-step AI workflows with frameworks such as LangChain, CrewAI, AutoGen, or similar. Build evaluation, observability, and safety mechanisms for LLM systems.
Multimodal AI: Integrate multimodal models (vision + text) to understand video and translate it into structured form.
Scale & Infrastructure: Ensure robustness, security, and high availability on AWS/Kubernetes. Contribute to distributed systems that handle real-time data and AI workloads efficiently.
Collaborate: Work closely with Product and Design to translate GenAI capabilities into stable, scalable production features.

Required AI Developer
Who we are:
We are a TLV-based startup thats on a mission to change the work experience for 80% of the worlds global workforce - the deskless employees.
Our business management platform helps thousands of businesses thrive by taking away the daily hustle and complexities of managing teams, so they can have the peace of mind to grow and run their business.
About the Role:
Were looking for a highly motivated AI Developer to help design, build, and deploy intelligent agentic systems across our product ecosystem. In this role, you'll work at the intersection of machine learning, backend systems, and modern frontend technologies to deliver AI-first features that feel magical to users.

This is a hands-on, cross-functional role ideal for engineers who love building full-fledged features-from data pipelines and LLM orchestration to intuitive UI experiences-with a strong product mindset.

Responsibilities:
AI Agent Design & Integration:
Design and implement autonomous or semi-autonomous agents using LLMs (e.g., OpenAI, Anthropic, open-source models).
Work with prompt engineering, RAG pipelines, and tool/plugin integrations to enable agents to interact with internal and external systems.
Build scalable agent runtimes and orchestration layers (e.g., LangChain, Semantic Kernel, ReAct-based agents).
Fullstack Product Development:
Own full-stack features end-to-end: from backend APIs and data models to React-based frontend interfaces.
Integrate AI/agent capabilities into customer-facing products with clean UX and measurable performance.
Collaborate closely with design, product, and data teams to bring ideas from concept to production.
Systems & Infrastructure:
Build and maintain backend services and pipelines that support AI agents, including vector search, embeddings, function calling, and observability.
Optimize inference flows for performance and cost, potentially using streaming, caching, or local model inference.
Ensure systems are secure, reliable, and compliant with InfoSec standards.
Experimentation & Continuous Improvement:
Rapidly prototype and iterate on new AI capabilities and user experiences.
Analyze performance and usage metrics to drive product and model improvements.
Stay up to date with the evolving AI toolchain and emerging agent architectures.

we are looking for a Senior AI Engineer.
Responsibilities:
Identify high-impact opportunities to leverage AI and AI agents to automate and optimize internal workflows.
Design, architect, and implement end-to-end AI-driven solutions - from problem definition to production deployment and adoption.
Integrate AI systems with internal platforms, services, APIs, and third-party tools.
Collaborate with technical and non-technical stakeholders to translate business needs into practical, scalable solutions.
Take full ownership of solutions, including iteration, performance optimization, monitoring, and long-term maintenance.
Evaluate and adopt new AI tools, frameworks, and methodologies with a pragmatic, business-oriented approach.

We are looking for an AI Builder to design, build, and maintain agentic AI-driven workflows that streamline and automate internal business processes across the company.
This role focuses on initiating and orchestrating AI agents, integrating them with existing systems, and reducing manual effort across Sales, Marketing, Order Management, Finance, HR, and more.
You will work closely with the Information Systems team and business stakeholders to turn repetitive, human-dependent processes into reliable, AI-powered flows.
This is a hands-on builder role, not a research or pure data science position.
Key Responsibilities:
Agentic AI & Automation:
Design and implement agent-based AI workflows to automate internal processes end-to-end
Build multi-step AI agents capable of:
Decision-making
Tool usage (APIs, SaaS tools, internal systems)
Escalation and exception handling
Continuously optimize AI flows to reduce human involvement while maintaining control and auditability
AI Tooling & Platforms:
Implement solutions using Agentic AI platforms, tools and frameworks, such as: OpenAI / Azure OpenAI / Copilot Studio / Gemini Enterprise, AWS bedrock (AgentCore), SFDC Agentforce
Low-code / no-code automation tools with AI capabilities (e.g., Make, Workato, UiPath, n8n)
Select and evaluate new AI tools to support internal automation initiatives
Systems Integration:
Integrate AI agents with internal systems such as: CRM (SFDC), ERP (NetSuite), ticketing systems (Jira)
Identity and access management
Monitoring, logging, and knowledge bases
Build and consume APIs to enable agent actions and data retrieval
Governance, Reliability & Security:
Ensure AI workflows comply with security, privacy, and compliance requirements
Implement guardrails, approvals, logging, and human-in-the-loop mechanisms where needed
Monitor AI performance, errors, hallucinations, and drift
Collaboration & Enablement:
Partner with business owners to identify automation opportunities
Translate business requirements into AI-driven solutions
Document AI flows, decision logic, and operational runbooks
Educate internal teams on AI capabilities and limitations.

Required Machine Learning Engineer II - GenAI Applications
26947
About the team:
This opening is for the GenAI Applications Team within the Data & AI Marketplace department.
The GenAI Applications team is responsible for designing and delivering agentic, ML-powered solutions for some of our most impactful products, including booking search experiences, trip planning, and trip helpfulness. The team builds AI-driven applications and conversational agents, such as chatbots and intelligent assistants, that significantly enhance the end-to-end customer experience.
Role Description:
As a Machine Learning Engineer, you will work closely with experienced engineers and ML scientists to build scalable, production-grade GenAI applications. Your work will focus on designing, training, and deploying ML systems leveraging LLMs,, recommendation systems, and agent-based architectures, using state-of-the-art technologies. These solutions will directly power customer-facing experiences and play a key role in shaping the future of AI-driven travel products.
Key Job Responsibilities and Duties:
Deploying machine learning models: Design, develop and deploy in collaboration with scientists, scalable machine learning models and algorithms that provide content related insights and generative AI applications, ensuring scalability, efficiency, and accuracy.
Evaluating possible architecture solutions by taking into account cost, business requirements, emerging technologies, and technology requirements, like latency, throughput, and scale.
Generative AI Development: Contribute to the development of generative models such as GPT (Generative Pre-trained Transformer) variants or similar architectures for creative content generation, Q&A, chatbots, translation or other innovative applications.
Deployment and integration: Work closely with software engineers to integrate machine learning models into production systems. Ensure seamless deployment and efficient model inference in real-time environments. Collaborate with DevOps to implement effective monitoring and maintenance strategies.
Owning a service end to end by actively monitoring application health and performance, setting and monitoring relevant metrics and acting accordingly when violated.
Maintain clean, scalable code, ensuring reproducibility and easy integration of models into production environments, including CI/CD.
Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions.

Join our companys AI research group, a cross-functional team of ML engineers, researchers and security experts building the next generation of AI-powered security capabilities. Our mission is to leverage large language models to understand code, configuration, and human language at scale, and to turn this understanding into security AI capabilities which will drive our company AI future security solutions.
We foster a hands-on, research-driven culture where youll work with large-scale data, modern ML infrastructure, and a global product footprint that impacts over 100,000 organizations worldwide.
Key Responsibilities
Your Impact & Responsibilities
As a Senior ML Research Engineer, you will be responsible for the end-to-end lifecycle of large language models: from data definition and curation, through training and evaluation, to providing robust models that can be consumed by product and platform teams.
Own training and fine-tuning of LLMs / seq2seq models: Design and execute training pipelines for transformer-based models (encoder-decoder, decoder-only, retrievalaugmented, etc.), and fine-tune open-source LLMs on our company-specific data (security content, logs, incidents, customer interactions).
Apply advanced LLM training techniques such as instruction tuning, preference / contrastive learning, LoRA / PEFT, continual pre-training, and domain adaptation where appropriate.
Work deeply with data: define data strategies with product, research and domain experts; build and maintain data pipelines for collecting, cleaning, de-duplicating and labeling large-scale text, code and semi-structured data; and design synthetic data generation and augmentation pipelines.
Build robust evaluation and experimentation frameworks: define offline metrics for LLM quality (task-specific accuracy, calibration, hallucination rate, safety, latency and cost); implement automated evaluation suites (benchmarks, regression tests, redteaming scenarios); and track model performance over time.
Scale training and inference: use distributed training frameworks (e.g. DeepSpeed, FSDP, tensor/pipeline parallelism) to efficiently train models on multi-GPU / multi-node clusters, and optimize inference performance and cost with techniques such as quantization, distillation and caching.
Collaborate closely with security researchers and data engineers to turn domain knowledge and threat intelligence into high-value training and evaluation data, and to expose your models through well-defined interfaces to downstream product and platform teams.

we are growing and we want you to join us! Have you ever wanted to work with an industry leader? Well, how about an industry creator?! were not just the leader in digital adoption, we started the digital adoption revolution - enabling organizations to measure, drive, and act to ultimately maximize the impact of their digital transformation and accelerate the return on their software investment. And with over 2,000 clients, including more than a third of Fortune 500 companies, working with us on a daily basis, were changing how enterprises interact with their technology.
As an AI Solution Engineer, youll help shape how AI-driven automations are applied across our internal systems. Youll build and operate AI agents and workflows that connect data, tools, and processes, enabling teams across the organization to work more efficiently at scale.
Youll work closely with IT and platform stakeholders, as well as teams across the company, combining AI capabilities with automation tools, APIs, and code to deliver solutions that move from experimentation to reliable, long-term operation.

What will you actually be doing?
Design and develop AI automations, agents, and agentic workflows that automate and augment internal business processes.
Develop and maintain integrations and workflows using tools like n8n, Workato, and other automation or orchestration platforms.
Write JavaScript-based logic (and Python where relevant) to extend low-code/no-code tools, integrate APIs, and orchestrate AI-driven flows.
Build scalable, secure, and reliable automations, including monitoring, logging, and guardrails for AI behavior.
Implement advanced prompting and agent patterns, such as structured outputs, tool/function calling, and RAG.
Optimize workflows using data, telemetry, and feedback to improve reliability, performance, and business impact.
Collaborate closely with the IT team to ensure solutions align with enterprise architecture, security, and compliance standards.
**Engagement will initially be via an employment agency for the first few months, with the possibility of transitioning to direct company employment at a later stage

This is a role for someone who wants to shape the fundamentals of our platform, both technically and strategically. Youll lead the design of scalable GenAI systems and connectivity to other platforms and tools, define effective AI best practices, mentor engineers, and influence how AI is built across the company all while staying deeply hands-on.

Its about ownership and impact: building, leading, and shaping impact end-to-end.
What You'll Do:
Lead the architecture and implementation of scalable GenAI solutions.
Define and enforce AI best practices: evaluation, privacy, responsible AI, and performance.
Drive AI strategy across teams and products.
Read, interpret, and apply cutting-edge AI research.
Design infrastructure that enables AI at scale-from zero-to-one to production.
Mentor engineers and grow technical leadership across the organization.