לוח משרות דרושים NLP/Machine Learning עבודה היברידית

Were looking for a Senior AI/ML Architect to help shape how our customers implement AI. In this role, youll design scalable, end-to-end ML solutions, from classical models to generative AI, and guide customers from concept to production. Youll work closely with C-level leaders, data scientists, and engineers to deliver impactful, real-world AI outcomes.
📍 Work location - hybrid from Tel Aviv
If you are interested in this opportunity, please submit your CV in English.
Responsibilities
Design and deliver end-to-end ML solutions, including classical ML, deep learning, and generative AI
Create technical proposals, reference architectures, and deployment plans
Lead workshops, POCs, and architecture reviews with startup customers
Translate business needs into scalable ML systems
Implement distributed training workflows and LLM serving infrastructure
Build MLOps pipelines for tracking, versioning, and monitoring
Provide technical guidance to customers and internal teams
Stay up to date on ML trends and contribute to internal enablement
Frequent travels - locally (on-demand to meet with customers and partners and attend local events) and abroad (at least once a quarter).

If youre looking to build something big, solve real problems, and grow fast-wed love to meet you.

You will own AI infrastructure and agentic interaction layer on top of our Data Platform.

The data layer is the product.
The AI layer is the multiplier.
The interaction model between them is the future.

Your responsibility is to design and build:

The AI infrastructure that enables safe, scalable, production-grade AI features

The ability to interact with Data Layer through LLMs and agentic workflows

Guardrails, governance, and system boundaries for AI-driven data access

The foundations that allow AI to be powerful without compromising correctness or trust

Required Senior Machine Learning Platform Engineer
What will you do?
As a Senior ML Platform Engineer, you will design and build core backend systems and ML infrastructure that power research, training, and online inference at scale.
You will work at the intersection of backend engineering, data engineering, and machine learning, partnering closely with data scientists to transform research needs into production-grade platform capabilities with real business impact.
Key Responsibilities
Design, build, and own backend systems that support ML workflows, data processing, and core platform services
Architect and optimize systems for performance, scalability, reliability, and cost efficiency in production ML environments
Collaborate closely with data scientists to implement, iterate, and evolve ML platform capabilities
Build and maintain large-scale data ingestion, processing, and training pipelines
Ensure systems are production-ready through testing, observability, monitoring, and performance optimization
Investigate and resolve infrastructure and performance issues affecting critical ML and business KPIs.

Required Machine Learning Platform Engineer
Team Overview
The ML Platform team builds the core infrastructure, services, and systems that enable machine learning at scale across the company.
We own the full lifecycle of ML workloads - from research and experimentation to reliable, large-scale production deployment.
The team focuses on:
Building scalable backend and infrastructure solutions for end-to-end ML workflows
Developing core ML platform components, including data processing, training, evaluation, and deployment
Empowering data scientists and product teams with robust, observable, and high-performance ML infrastructure
Solving complex challenges around scale, latency, throughput, reliability, and cost efficiency
Continuously evolving the ML platform by adopting new technologies and improving system performance and stability
What will you do?
As a ML Platform Engineer, you will design and build core backend systems and ML infrastructure that power research, training, and online inference at scale.
You will work at the intersection of backend engineering, data engineering, and machine learning, partnering closely with data scientists to transform research needs into production-grade platform capabilities with real business impact.
Key Responsibilities
Design, build, and own backend systems that support ML workflows, data processing, and core platform services
Architect and optimize systems for performance, scalability, reliability, and cost efficiency in production ML environments
Collaborate closely with data scientists to implement, iterate, and evolve ML platform capabilities
Build and maintain large-scale data ingestion, processing, and training pipelines
Ensure systems are production-ready through testing, observability, monitoring, and performance optimization
Investigate and resolve infrastructure and performance issues affecting critical ML and business KPIs.

Required Machine learning operations engineer
Your Mission:
As an MLOps Engineer, your mission is to design, build, and operate the platforms that power our machine learning and generative AI products spanning real-time use cases such as large-scale fraud scoring, MCP & agentic workflows support. Youll create reliable CI/CD for models and Agents, robust data/feature pipelines, secure model serving, and comprehensive observability. You will also support our agentic AI ecosystem and Model Context Protocol (MCP) services so that models can safely use tools, data, and actions across.
You will partner closely with Data Scientists, Data/Platform Engineers, Product, and SRE to ensure every model from classic ML to LLM/RAG agents moves from prototype to production with strong reliability, governance, cost efficiency, and measurable business impact.
Responsibilities:
Operate & Develop ML/LLM platforms on Kubernetes + cloud (Azure; AWS/GCP ok) with Docker, Terraform, and other relevant tools
Manage object storage, GPUs, and autoscaling for training & low-latency model serving
Manage cloud environment, networking, service mesh, secrets, and policies to meet PCI-DSS and data-residency requirements
Build end-to-end CI/CD for models/agents/MCP tooling (versioning, tests, approvals)
Deliver real-time fraud/risk scoring & agent signals under strict latency SLOs.
Maintain MCP servers/clients: tool/resource definitions, versioning, quotas, isolation, access controls
Integrate agents with microservices, event streams, and rule engines; provide SLAs, tracing, and on-call runbooks
Measure operational metrics of ML/LLM (latency, throughput, cost, tokens, tool success, safety events)
Enforce governance: RBAC/ABAC, row-level security, encryption, PII/secrets management, audit trails.
Partner with DS on packaging (wheels/conda/containers), feature contracts, and reproducible experiments.
lead incident response and post-mortems.
Drive FinOps: right-sizing, GPU utilization, batching/caching, budget alerts.