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.