דרושים » AI » AI Engineering Team Lead - Applied AI Engineering Group

It starts with you - a senior AI engineer focused on building production-grade agentic systems: autonomous agents, MCP tools, agentic workflows, and rigorous evaluation.
If you want to grow your skills building AI products for mission-critical AI, join mission - this role is for you.
:Responsibilities
Design and build single- and multi-agent systems with planning, memory, and tool use.
Implement and operate MCP tools and servers with secure schemas and permissions.
Build agentic workflows using LangChain, LangGraph, or equivalent frameworks.
Integrate LLMs via SDKs; manage prompts, structured outputs, and tool calling.
Define and run LLM evals (quality, correctness, latency, cost, regressions).
Add observability and reliability (logging, tracing, retries, state management).
Optimize performance and cost; take systems to production.
Mentor engineers and establish agentic best practices.

It starts with you - ML Engineering Team Lead responsible for leading a team delivering end-to-end ML systems. This role combines people management with ownership of model training, evaluation, deployment, and ML platform standards
If you want to grow your skills building AI products for mission-critical AI, join mission - this role is for you. :
: Responsibilities
Lead and mentor ML Engineers delivering production ML systems.
Own technical direction for model architectures, training pipelines, and evals.
Review designs and code; ensure scalability, reliability, and data quality.
Define standards for experimentation, reproducibility, and monitoring.
Partner with product and platform teams on roadmap and prioritization.
Balance hands-on technical contribution with people management.
Build a culture of measurement, rigor, and continuous improvement.

Were on the lookout for a driven and experienced hands-on Engineering Team Leader to lead a group of engineers building the AI application foundation behind our next-generation cyber-AI product.
This team owns the core backend and frontend infrastructure that powers all user-facing applications - as well as the agentic and conversational frameworks that bring intelligence and automation into the experience.
As a hands-on leader, youll guide a talented team of engineers in designing and evolving the systems that enable every other Engineering group to create fast, scalable, and intelligent apps on top of our platform.
:Responsibilities
Lead a multidisciplinary team responsible for backend services, frontend frameworks, and AI agent infrastructure - the technical bedrock of our product experience.
Mentor engineers, grow the team, and foster a culture of technical excellence and innovation.
Design and build robust frameworks and systems that power all customer-facing applications.
Develop the agentic and conversational architecture enabling LLM-powered user interactions and intelligent workflows.
Collaborate with AI research, data, and product teams to seamlessly integrate AI-driven capabilities into production systems.
Define and drive the technical roadmap, ensuring scalability, developer productivity, and rapid iteration.

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 seeking a Product Manager for AI Platform, to lead most critical evolution: the transformation to an Agentic Platform.

This role operates as a "Startup within a Startup." You will own the end-to-end AI domain, from building the underlying technical infrastructure to launching the interfaces our customers interact with.

You will drive the entire product lifecycle: bringing innovative ideas to the table, executing on early versions, and collaborating closely with internal teams and design partners to validate value. Your ultimate goal is to mature these capabilities from early concepts into core platform standards, driving mass adoption across our entire user base.

You will serve as the knowledge center for the organization, guiding how the entire platform must evolve to support the next generation of autonomous engineering.

What You Will Be Doing

Lead AI product domain from ideation to launch of customer-facing AI interfaces and agents
Design and manage AI Agents platform, enabling customers to create, manage, and run AI agents
Drive ecosystem initiatives to make the platform native to agents through API improvements, MCP integration, and agent-accessible protocols
Define strategic direction for making "AI-Native," guiding data architecture for effective AI agent interaction
Own product roadmap and prioritization, balancing immediate needs with long-term Agentic architecture investments
Advocate across product and R&D teams to integrate AI-native interfaces and drive AI-first development mindset
Identify high-impact opportunities through customer discovery and experimentation with cutting-edge AI technologies
Collaborate with engineering and design to translate AI capabilities into intuitive, trust-building user experiences
Analyze adoption metrics and feedback to optimize AI performance and increase trust in autonomous workflows
Act as company AI center of excellence, enabling Sales, Marketing, and CS teams to articulate Agentic Engineering value

We are looking for a highly skilled AI Engineer with a strong engineering mindset to bridge the gap between research and production.
In this role, you will be responsible for validating AI models developed by our Data Science team against real-world production systems, and then leading their optimization, deployment, and ongoing maintenance.
You will be part of the R&D team, working closely with engineers, data scientists, and product managers to ensure our AI solutions are scalable, reliable, and deliver long-term value.
If you enjoy working at the intersection of AI and engineering - bringing models to life in production, optimizing for performance, and building reliable systems - this role is for you!
Responsibilities
Lead the transition of AI models from proof-of-concept to full-scale production, ensuring they meet architectural, scalability, and performance standards.
Build observability and troubleshooting tools for AI services in production, including logging, performance tracking, and failure analysis pipelines.
Optimize inference performance, including latency, resource usage, and throughput, while maintaining model quality.
Manage model versioning and deployment readiness, including handoff processes, rollback plans, and configuration management.
Partner with the Data Science team to assess model readiness for production, validate input and output compatibility, and ensure assumptions align with real-world system behavior.
Collaborate cross-functionally with DevOps, backend engineers and data scientists to ensure scalable, secure, and cost-effective deployment of ML models.

Our Networking Research group is seeking a forward-thinking Engineering Manager with a product leadership approach to manage a team of Software Engineers, AI Agent Developers, and Researchers.

In this role, you will bridge the gap between brand-new AI research and practical engineering. You will lead the charge in integrating autonomous AI agents into the daily workflows of our architects, hardware designers, and researchers. You will be responsible for understanding internal engineering bottlenecks, researching the latest academic and industry advancements, and translating them into deployed production tools using our AI stack.

What you'll be doing:

Team Leadership & Strategy: Lead and mentor a mixed team of SW engineers and researchers, balancing the agility of research with the responsibility of production software engineering.

Product Engineering: Partner with internal engineering teams (Hardware, Architecture, Research) to analyze their workflows, identify friction points, and architect Agentic AI solutions to accelerate their work.

Applied Research: Actively monitor the latest AI trends, academic papers, and technical blogs (LLMs, Agents, RAG). Translate these insights into concrete features and tools for the team.

Agent Architecture: Be responsible for the design of scalable frameworks for AI agent interaction, ensuring they can reason, plan, and orchestrate tasks within complex engineering environments.

SW-Focused MLOps: Define and implement the software lifecycle for AI models - from fine-tuning to deployment - utilizing our AI deployment tools and ensuring seamless CI/CD.

Production Deployment: Ensure reliable operation of agent fleets, optimizing for latency and resource consumption while maintaining security and access controls.

We are looking for an experienced AI Backend Engineer to join our fast-growing company during an exciting stage of innovation. In this role, you'll design and develop Agent and LLM-based features, engineer and optimize context and high-quality prompts, implement agentic architectures in production and evaluate and monitor applications effectively. If you're passionate about staying at the forefront of Large Language Model AI applications and collaborating with a team of experts, wed love to hear from you!

What Will You Do?
Take part in all development aspects, from design to production, of Agent and LLM-based features and applications.
Take part in all the development and planning aspects of a backend application, understanding scale, performance, observability.
Craft and optimize high-quality prompts to enhance application functionality and output quality.
Develop datasets and benchmarks to evaluate and monitor LLM-based applications effectively.
Stay up to date with state-of-the-art research in the field of Large Language Models and apply relevant advancements to improve our applications.
Collaborate closely with other team members and contribute to a culture of innovation and continuous improvement.
Mentor new team members and share your expertise to strengthen the team's collective knowledge.

we are seeking a highly skilled Senior AI Engineer to design and implement complex, multi-agent systems powered by LLMs.
In this role, you will build and orchestrate AI agents using LangGraph and LangChain, manage stateful conversations, and support scalable RAG pipelines that connect external knowledge sources to reasoning agents. Youll work closely with product and backend teams to deliver real, production-grade AI capabilities.
Responsibilities:
Agent Orchestration: Design, build, and maintain networks of LLM-powered agents that collaborate, share context, and reason together to execute complex workflows.
LangGraph Architecture: Implement and manage LangGraph graphs that handle message memory, context persistence, and multi-step task coordination.
RAG Pipelines: Optimize RAG systems to enrich agent reasoning with relevant, up-to-date information.
Prompt & API Composition: Build modular prompt templates and API calls that agents use for reasoning, retrieval, and action execution.
Integration & Deployment: Work with engineers to integrate the agentic framework into production systems via APIs and backend services.
Evaluation & Iteration: Implement evaluation frameworks to measure performance, reliability, and consistency of AI responses across multi-agent pipelines.
Cross-Team Collaboration: Partner with product and engineering teams to align AI behaviors with real user workflows and KPIs.

Were hiring a Machine Learning Engineering Manager to guide and grow a high-impact ML team driving AI-powered innovation across B2B SaaS platform. Youll lead the design and delivery of AI solutions while mentoring engineers and setting the technical direction for AI-first development at scale.
This is a leadership role with a balance of hands-on engineering and team management, perfect for someone who thrives on solving technical challenges, inspiring a team, and shaping the future of AI in fintech automation.
What You Will Do:
Lead & Mentor: Manage, mentor, and grow a team of ML engineers, fostering technical excellence and career development.
Set Technical Direction: Define the ML strategy, ensuring best practices in architecture, frameworks, and operationalization.
Build and deploy AI-based solutions: Oversee the development and deployment of GenAI/LLM-powered solutions that address real-world challenges across products.
Scale & Operationalize: Establish scalable ML infrastructure, CI/CD, observability, and data pipelines for high-availability production systems.
Collaborate Cross-Functionally: Partner with product managers, engineers, and business stakeholders, clearly communicate progress, challenges, and outcomes.