לוח משרות דרושים NLP/Machine Learning עבודה היברידית

We are looking for a talented Algorithm Engineer to join our Cognitive AI team. In this role, you will design, build, and optimize LLM-based agents that power real-time problem solving. You will be hands-on in Python, research-driven, and passionate about creating intelligent, scalable AI solutions that make a real impact.
Responsibilities:
Design, develop, and optimize LLM-based agents for real-world production use cases.
Build intelligent agent frameworks that orchestrate reasoning across tools, APIs, and knowledge bases.
Deploy and maintain LLM applications in production, ensuring performance, scalability, and reliability.
Research new approaches in LLMs and agent architectures, and integrate cutting-edge techniques into production systems.
Collaborate with cross-functional teams (product, engineering, data) to translate business needs into AI solutions.
Continuously monitor and optimize deployed models, diagnosing and resolving bottlenecks.
Think creatively about complex problems and deliver technological Innovation

We are seeking a hands-on Tech Lead to be the first technical hire for our newly formed ML team. This is a unique ground-floor opportunity to build something from scratch and have direct influence on the technical direction of our ML capabilities. This role combines deep technical expertise with foundational leadership, requiring someone who can architect solutions, establish technical standards, and lay the groundwork for future team growth. As our founding ML tech lead, you'll have the opportunity to shape our technology choices, define our technical practices, and build the fundamental skill set that will guide the team as we scale.
Hybrid
Full-time
What youll do: 
Shape the technical foundation: As our first ML hands on hire, choose and establish the team's core technology stack, frameworks, and development practices together with the ML leader
Build from the ground up: Design and implement our initial ML infrastructure, data pipelines, and model deployment frameworks
Define technical standards: Establish coding standards, best practices, and processes that will guide future team members
Hands-on development: Lead by example through direct development and deployment of ML models and data science solutions
Partner with stakeholders: Work closely with business teams to identify initial ML opportunities and build foundational proof-of-concept models
Establish MLOps practices: Set up our model development, testing, deployment, and monitoring workflows from scratch
Recruit and onboard: As the team grows, play a key role in technical interviews and help onboard new ML engineers and data scientists
Create team culture: Help establish the team's technical culture, learning practices, and collaboration methods
Technology evaluation: Research and select the tools, platforms, and technologies that will form our ML tech stack
Document and share knowledge: Create foundational documentation and knowledge-sharing practices for the growing team.

Required Senior ML Backend Engineer
Tel Aviv
As a Senior Machine Learning Backend Engineer, you'll architect and build the high-performance backend systems that power our AI at massive scale, craft real-time ML serving innovative solutions, and play a pivotal role in our mission to revolutionize the way companies understand their customer interactions. We're seeking a Java professional who can leverage their deep knowledge to drive our backend development to new heights.
You will be responsible for:
ML Serving Architecture: Design and implement high-performance inference APIs and model serving backends using Java & Python.
Model Lifecycle Management: Build systems for model versioning, A/B testing, canary deployments, and automated rollbacks.
Integration Platform: Create robust APIs and SDKs that enable product teams to seamlessly integrate AI capabilities.
Observability & Monitoring: Build comprehensive metrics, logging, and tracing systems for ML workloads.
Cross-team Leadership: Mentor engineers & researchers, drive technical decisions, and influence platform architecture across the organization.

We are seeking an experienced AI Development Expert with deep technical expertise, strong system-level thinking, and a proven ability to define and implement AI-based solutions across an organization. This role involves shaping AI system architecture, selecting appropriate technologies, establishing standards, building infrastructure, and advancing innovation in areas such as Machine Learning, Natural Language Processing, and other intelligent applications.
How will you make an impact?
Write high-quality, efficient Python code, including model development, data processing scripts, and MLOps infrastructure.
Guide and advise technical teams on architecture selection, development methodologies, and MLOps practices.
Partner in defining end-to-end AI solution architecture from infrastructure to product.
Support selection and implementation of models, platforms, and advanced AI/ML tools.
Manage risks and address scalability, security, and regulatory aspects of AI solutions.
Write architecture documents, conduct technology reviews, and contribute to strategic projects.
Research and support new technologies (Generative AI, LLMs, CV, and more) according to organizational needs.
Collaborate with development, product, and data teams to optimally integrate AI solutions into existing and new products.

The OpenShift team is looking for a Machine Learning Engineer with experience in building, scaling, and monitoring AI/ML systems to join our rapidly growing engineering team. Our focus is to create a platform, partner ecosystem, and community by which enterprise customers can solve problems to accelerate business success using AI. This is a very exciting opportunity to shape the observability and reliability of GenAI workloads, contribute to the development of the RHOAI product, participate in open source communities, and be at the forefront of the exciting evolution of AI. Youll join an ecosystem that fosters continuous learning, career growth, and professional development.
As a core ML engineer for one of our OpenShift AI teams, you will have the opportunity to design and build systems that monitor, validate, and improve AI model performance in production. You will work as part of an evolving development team to rapidly design, secure, build, test, and release new capabilities. The role is primarily an individual contributor who collaborates closely with other ML engineers, software developers, and cross-functional teams. You should have a passion for observability, MLOps, and building robust systems for real-world AI.
What you will do:
Architect and lead implementation of new features and solutions for RHOAI, focusing on observability, insights, and optimizations for large-scale GenAI workloads running on Kubernetes
Innovate in the MLOps domain by participating in leading upstream communities such as llm-d
Provide technical vision and leadership on critical and high impact projects
Use CI/CD best practices to deliver solutions as productization efforts into RHOAI
Proactively utilize AI-assisted development tools (e.g., GitHub Copilot, Cursor, Claude Code) for code generation, auto-completion, and intelligent suggestions to accelerate development cycles and enhance code quality.
Collaborate with product management, other engineering and cross-functional teams to analyze and clarify business requirements
Collaborate with cross-functional teams to identify opportunities for AI integration within the software development lifecycle, driving continuous improvement and innovation in engineering practices
Contribute to a culture of continuous improvement by sharing recommendations and technical knowledge with team members
Communicate effectively to stakeholders and team members to ensure proper visibility of development efforts
Represent RHOAI in external engagements including industry events, customer meetings, and open source communities
Explore and experiment with emerging AI technologies relevant to software development, proactively identifying opportunities to incorporate new AI
Mentor, influence, and coach a distributed team of engineers.

The OpenShift AI team is looking for a Machine Learning Engineer with experience in building, scaling, and monitoring AI/ML systems to join our rapidly growing engineering team. Our focus is to create a platform, partner ecosystem, and community by which enterprise customers can solve problems to accelerate business success using AI. This is a very exciting opportunity to shape the deployment and reliability of GenAI workloads, contribute to the development of the RHOAI product, participate in open source communities, and be at the forefront of the exciting evolution of AI. Youll join an ecosystem that fosters continuous learning, career growth, and professional development.
As a core ML engineer for one of our OpenShift AI teams, you will have the opportunity to design and build systems that monitor, validate, and improve AI model performance in production. You will work as part of an evolving development team to rapidly design, secure, build, test, and release new capabilities. The role is primarily an individual contributor who collaborates closely with other ML engineers, software developers, and cross-functional teams. You should have a passion for observability, MLOps, and building robust systems for real-world AI.
Our commitment to open source innovation extends beyond our products - its embedded in how we work and grow. we embrace change especially in our fast-moving technological landscape and have a strong growth mindset. That's why we encourage our teams to proactively, thoughtfully, and ethically use AI to simplify their workflows, cut complexity, and boost efficiency. This empowers our associates to focus on higher-impact work, creating smart, more innovative solutions that solve our customers' most pressing challenges.
What you will do:
Design and build observability and assistance tools to help customers optimize large-scale AI initiatives running on Kubernetes
Innovate in the MLOps and AI observability and deployment optimization domains by contributing to upstream communities
Collaborate with product, engineering, and research teams to improve model trust and performance
Write unit and integration tests and work with quality engineers to ensure product quality
Use CI/CD best practices to deliver solutions into RHOAI as part of our productization efforts
Proactively utilize AI-assisted development tools (e.g., GitHub Copilot, Cursor, Claude Code) for code generation, auto-completion, and intelligent suggestions to accelerate development cycles and enhance code quality.
Contribute to a culture of continuous improvement by sharing technical knowledge and insights
Communicate effectively with stakeholders and team members to ensure visibility of ML performance
Represent RHOAI in external engagements including open source communities and customer meetings
Mentor and guide junior engineers and contribute to team growth
Explore and experiment with emerging AI technologies relevant to software development, proactively identifying opportunities to incorporate new AI.

Required Principal Machine Learning Engineer GenAI Benchmarking & Validation Infrastructure
The Principal Machine Learning Engineer GenAI is responsible for hands-on design, development, and operation of large-scale systems and tools for AI model benchmarking, optimization, and validation.
Unlike traditional ML Engineers focused mainly on training models, this role centers on building, running, and continuously improving the infrastructure, automation, and services that enable rigorous, repeatable, and production-grade model evaluation at scale.
This is a hands-on principal role that combines strategic technical leadership with active engineering execution.
You will own the architecture, implementation, and optimization of benchmarking and validation capabilities across our AI ecosystem. This includes architecting Validation-as-a-Service platforms, delivering high-performance benchmarking pipelines, integrating with leading GenAI frameworks, and setting industry standards for model evaluation quality and reproducibility.
The role demands deep GenAI domain expertise, architectural foresight, and direct coding involvement to ensure evaluation platforms are flexible, extensible, and optimized for real-world, large-scale use.
What you will do
Architect and lead scalable benchmarking pipelines for LLM performance measurement (latency, throughput, accuracy, cost) across multiple serving backends and hardware types.
Build optimization & profiling tools for inference performance, including GPU utilization, memory footprint, CUDA kernel efficiency, and parallelism strategies.
Develop Validation-as-a-Service platforms with APIs and self-service tools for standardized, on-demand model evaluation.
Integrate and optimize model serving frameworks (vLLM, TGI, LMDeploy, Triton) and API-based serving (OpenAI, Mistral, Anthropic) in production environments.
Establish dataset & scenario management workflows for reproducible, comprehensive evaluation coverage.
Implement observability & diagnostics systems (Prometheus, Grafana) for real-time benchmark and inference performance tracking.
Deploy and manage workloads in Kubernetes (Helm, Argo CD, Argo Workflows) across AWS/GCP GPU clusters.
Lead performance engineering efforts to identify bottlenecks, apply optimizations, and document best practices.
Stay ahead of the GenAI ecosystem by tracking emerging frameworks, benchmarks, and optimization techniques, and integrating them into the platform.