דרושים » הנדסה » MLOps Engineer- 2548

We are looking for a Senior ML Engineer.
As a Senior ML Engineer , you will lead the design, deployment, and maintenance of production-ready machine learning systems that power legal insights at scale. Youll work across disciplinesintegrating LLMs, building pipelines, and optimizing infrastructureto deliver real-world impact.
Youll gain full ownership over the systems you build, from selecting the tools and platforms to deploying services in production. Youll work closely with DevOps and Data Engineering to ship robust solutions that drive meaningful change in the world.
Responsibilities :
Design and implement production-grade ML systems, including APIs, batch jobs, and streaming pipelines using Databricks, MLflow, AWS/GCP.
Build and manage ML infrastructure, including data pipelines, model training, deployment, and monitoring.
Develop and maintain end-to-end ML/LLM pipelinesfrom data ingestion and labeling to synthetic data generation, model registry, and rollout.
Own and improve MLOps practices: automated testing, CI/CD, monitoring, alerting, and model governance.
Write clean, maintainable Python code and uphold best practices in engineering and documentation.
Collaborate with DevOps and Data Engineering teams to scale systems and improve performance.
Research and recommend the best tools, platforms, and practices to support ML at scale.

Required Senior MLOps Engineer
Realize your potential by joining the leading performance-driven advertising company!
As a Senior MLOps Engineer on the Infra group, youll play a vital role in develop, enhance and maintain highly scalable Machine-Learning infrastructures and tools.
About Algo platform:
The objective of the algo platform group is to own the existing algo platform (including health, stability, productivity and enablement), to facilitate and be involved in new platform experimentation within the algo craft and lead the platformization of the parts which should graduate into production scale. This includes support of ongoing ML projects while ensuring smooth operations and infrastructure reliability, owning a full set of capabilities, design and planning, implementation and production care.
The group has deep ties with both the algo craft as well as the infra group. The group reports to the infra department and has a dotted line reporting to the algo craft leadership.
The group serves as the professional authority when it comes to ML engineering and ML ops, serves as a focal point in a multidisciplinary team of algorithm researchers, product managers, and engineers and works with the most senior talent within the algo craft in order to achieve ML excellence.
How youll make an impact:
As a Senior MLOps Engineer Engineer, youll bring value by:
Develop, enhance and maintain highly scalable Machine-Learning infrastructures and tools, including CI/CD, monitoring and alerting and more
Have end to end ownership: Design, develop, deploy, measure and maintain our machine learning platform, ensuring high availability, high scalability and efficient resource utilization
Identify and evaluate new technologies to improve performance, maintainability, and reliability of our machine learning systems
Work in tandem with the engineering-focused and algorithm-focused teams in order to improve our platform and optimize performance
Optimize machine learning systems to scale and utilize modern compute environments (e.g. distributed clusters, CPU and GPU) and continuously seek potential optimization opportunities.
Build and maintain tools for automation, deployment, monitoring, and operations.
Troubleshoot issues in our development, production and test environments
Influence directly on the way billions of people discover the internet
Our tech stack:
Java, Python, TensorFlow, Spark, Kafka, Cassandra, HDFS, vespa.ai, ElasticSearch, AirFlow, BigQuery, Google Cloud Platform, Kubernetes, Docker, git and Jenkins.

Required Staff MLOps Engineer
Apply
Realize your potential by joining the leading performance-driven advertising company!
As a Staff MLOps Engineer on the Infra group, youll play a vital role in develop, enhance and maintain highly scalable Machine-Learning infrastructures and tools.
About Algo platform:
The objective of the algo platform group is to own the existing algo platform (including health, stability, productivity and enablement), to facilitate and be involved in new platform experimentation within the algo craft and lead the platformization of the parts which should graduate into production scale. This includes support of ongoing ML projects while ensuring smooth operations and infrastructure reliability, owning a full set of capabilities, design and planning, implementation and production care.
The group has deep ties with both the algo craft as well as the infra group. The group reports to the infra department and has a dotted line reporting to the algo craft leadership.
The group serves as the professional authority when it comes to ML engineering and ML ops, serves as a focal point in a multidisciplinary team of algorithm researchers, product managers, and engineers and works with the most senior talent within the algo craft in order to achieve ML excellence.
How youll make an impact:
As a Staff MLOps Engineer Engineer, youll bring value by:
Develop, enhance and maintain highly scalable Machine-Learning infrastructures and tools, including CI/CD, monitoring and alerting and more
Have end to end ownership: Design, develop, deploy, measure and maintain our machine learning platform, ensuring high availability, high scalability and efficient resource utilization
Identify and evaluate new technologies to improve performance, maintainability, and reliability of our machine learning systems
Work in tandem with the engineering-focused and algorithm-focused teams in order to improve our platform and optimize performance
Optimize machine learning systems to scale and utilize modern compute environments (e.g. distributed clusters, CPU and GPU) and continuously seek potential optimization opportunities.
Build and maintain tools for automation, deployment, monitoring, and operations.
Troubleshoot issues in our development, production and test environments
Influence directly on the way billions of people discover the internet
Our tech stack:
Java, Python, TensorFlow, Spark, Kafka, Cassandra, HDFS, vespa.ai, ElasticSearch, AirFlow, BigQuery, Google Cloud Platform, Kubernetes, Docker, git and Jenkins.

a well-funded AI startup on a mission to revolutionize passenger journeys with intelligent threat detection powered by machine learning and computer vision. Were building cutting-edge AI tools to ensure safer and more efficient travel.

We are seeking a highly motivated and experienced Data Engineer & MLOps Specialist to join our dynamic Data team. This is a pivotal role where you will design and implement scalable data solutions, develop automation for machine learning workflows, and manage critical infrastructure supporting our AI/ML initiatives.

Responsibilities:
Develop and maintain automated pipelines for data transformation, model inference, and monitoring.
Build and manage an annotation database to ensure high-quality labeled data for AI training.
Design and deploy infrastructure for automated ML inference and neural network analysis using tools like Voxel51 and ClearML.
Develop ETL pipelines to integrate production data into the data warehouse for analysis and reporting.
Work on local servers and in the AWS cloud, ensuring efficient and secure processing, storage, and deployment of data and ML models.

A chance to design and implement solutions from scratch, shaping the future of data and AI infrastructure.
Work with AI technology in a high-growth startup environment.
Opportunities for career growth and continuous learning.
Join us in building the future of intelligent security systems

We are seeking an exceptional Data Scientist to lead applied research in machine learning and drive the AI transformation . This pivotal role will shape the next generation of customer-facing products and internal tools, helping to redefine the trading experience through advanced AI.
Key Responsibilities:
Design and build end-to-end AI workflows including synthetic dataset generation, data preprocessing, model development, deployment, and agent orchestration.
Design and implement LLM (Large Language Model) evaluation frameworks to assess new AI models, techniques, and parameters.
Process and analyze large-scale structured and unstructured data from databases, APIs, and develop machine learning models for time series forecasting, regression, and classification tasks.
Design and implement enterprise-grade RAG systems using LLMs, vector databases, GraphRA, G, and intelligent agents.
Optimize AI-user interactions through advanced prompt engineering and retrieval strategies.
Translate cutting-edge AI research into practical, scalable tools for internal and customer-facing applications and collaborate with global and cross-functional R&D teams to drive innovation across product lines.
Apply MLOps and LLMOps best practices to ensure robust model training, deployment, and monitoring pipelines.

we are at the forefront of developing cutting-edge, Real-Time, mission-critical platforms in the fields of energy, utilities, and smart infrastructure. We are looking for a passionate and experienced DevOps Engineer with deep expertise in MLOps Azure DevOps, and ArgoCD, to help drive scalable deployments and intelligent automation in production environments.
Responsibilities:

* Design and maintain CI/CD pipelines in Azure DevOps for both traditional applications and ML workloads.
* Develop and manage MLOps workflows for model training, validation, deployment, and monitoring.
* Automate deployment of services using ArgoCD, Helm, and GitOps best practices.
* Manage and operate production-grade Kubernetes clusters (on-prem and Azure AKS).
* Implement Infrastructure as Code (IaC) using Terraform
* Collaborate closely with data scientists, software engineers, and infrastructure teams to support end-to-end delivery.
* Monitor system performance, availability, and reliability using tools like Prometheus Grafana, and EFK stack
* Ensure best practices in security, version control, logging, and compliance.
Why Join Us:

* Be part of a leading-edge technology company solving real-world challenges in energy and infrastructure.
* Work with a multidisciplinary team of experts in a dynamic and collaborative environment.
* Enjoy continuous professional development.
* Opportunity to work on impactful, large-scale projects with national and global reach.

As a Big Data & GenAI Engineering Lead within our company's Data & AI Department, you will play a pivotal role in building the data and AI backbone that empowers product innovation and intelligent business decisions. You will lead the design and implementation of our companys next-generation lakehouse architecture, real-time data infrastructure, and GenAI-enriched solutions, helping drive automation, insights, and personalization at scale. In this role, you will architect and optimize our modern data platform while also integrating and operationalizing Generative AI models to support go-to-market use cases. This includes embedding LLMs and vector search into core data workflows, establishing secure and scalable RAG pipelines, and partnering cross-functionally to deliver impactful AI applications.
As a Big Data & GenAI Engineering Lead in our company you will...
Design, lead, and evolve our companys petabyte-scale Lakehouse and modern data platform to meet performance, scalability, privacy, and extensibility goals.
Architect and implement GenAI-powered data solutions, including retrieval-augmented generation (RAG), semantic search, and LLM orchestration frameworks tailored to business and developer use cases.
Partner with product, engineering, and business stakeholders to identify and develop AI-first use cases, such as intelligent assistants, code insights, anomaly detection, and generative reporting.
Integrate open-source and commercial LLMs securely into data products using frameworks such as LangChain, or similar, to augment AI capabilities into data products.
Collaborate closely with engineering teams to drive instrumentation, telemetry capture, and high-quality data pipelines that feed both analytics and GenAI applications.
Provide technical leadership and mentorship to a cross-functional team of data and ML engineers, ensuring adherence to best practices in data and AI engineering.
Lead tool evaluation, architectural PoCs, and decisions on foundational AI/ML tooling (e.g., vector databases, feature stores, orchestration platforms).
Foster platform adoption through enablement resources, shared assets, and developer-facing APIs and SDKs for accessing GenAI capabilities.

We are seeking a skilled and passionate Machine Learning Engineer who combines strong software engineering expertise with a deep understanding of data science and machine learning. In this role, you will be instrumental in developing and improving our ML systems, ensuring that both the underlying infrastructure and the algorithms powering our models are optimized for performance and scalability.
You will collaborate closely with cross-functional teams, including data scientists and software engineers, to implement end-to-end machine learning models that drive impactful business outcomes. Additionally, your work will ensure the continued improvement of our ML platform, which supports algorithmic work across the company.
What will you do?
As a Senior Machine Learning Engineer, your mission will be:
Improve existing ML library & tools allowing to explore and analyze more and more data and provide accurate feedback on the activities.
Create, design, develop, test and monitor your code in production autonomously and reliably.
Design, develop, and implement ML models and algorithms with a focus on robustness, performance, and scalability.
Collaborate with data scientists to identify and apply effective machine learning techniques and strategies for improved system performance.
Mentor other team members to improve their autonomy and Software Engineering skills.
Collaborate with variety of teams for production cases to develop services from design to production.
Make sure the software is in good hands by writing, running and automating tests (unit, functional, load...).
Keep up to date with the latest Machine Learning technologies to make sure we constantly improve our ML system.

Were looking for an experienced MLOps / DevOps Engineer to design and manage the infrastructure powering large-scale machine learning systems. Youll be responsible for deploying GPU-heavy models (including LLMs) on cost-efficient, production-grade infrastructure, supporting both ML workflows and application artifact delivery.

Youll work with cutting-edge technologies like vLLM, Triton, SageMaker, ClearML, Karpenter, KEDA, and EKS, ensuring the right balance between performance, scalability, and cost.



What Youll Do

Deploy and manage LLMs and deep learning models using vLLM, Triton Inference Server, and custom API endpoints.

Build and maintain GPU-aware autoscaling clusters using AWS EKS, Karpenter, and KEDA, optimizing for cost-efficiency and performance.

Develop CI/CD pipelines using Jenkins and GitHub Actions to automate ML model delivery and application deployments.

Orchestrate training, fine-tuning, and inference jobs on AWS SageMaker and ClearML, with support for experiment tracking, versioning, and reproducibility.

Support backend teams in deploying app artifacts and runtime environments; implement rollback and release strategies.

Integrate observability tooling (e.g., Prometheus, Grafana, ELK, or OpenTelemetry) for both infrastructure and model performance.

Collaborate with SREs to enforce high availability, disaster recovery, and incident response procedures for mission-critical AI services.

At UVeye, we're on a mission to redefine vehicle safety and reliability on a global scale. Founded in 2016, we have pioneered the world's first fully automated suite of vehicle inspection systems. At the heart of this innovation lies our advanced AI-driven technology, representing the pinnacle of machine learning, GenAI, and computer vision within the automotive sector. With close to $400M in funding and strategic partnerships with industry giants such as Amazon, General Motors, Volvo, and CarMax, UVeye stands at the forefront of automotive technological advancement. Our growing global team of over 200 employees is committed to creating a workplace that celebrates diversity and encourages teamwork. Our drive for innovation and pursuit of excellence are deeply embedded in our vibrant company culture, ensuring that each individual's efforts are recognized and valued as we unite to build a safer automotive world.
We are seeking a highly motivated and skilled Release Engineer to join our AIOps group. In this role, you'll play a critical part in bridging the gap between development and operations, ensuring the seamless qualification, deployment, and monitoring of our AI algorithms and infrastructure, and be responsible for the end-to-end operationalization of our core technology.
A day in the life and how you’ll make an impact:
* Manage the end-to-end release process of machine learning algorithms and infrastructure components, from qualification through deployment.
* Validate and test new algorithm releases to ensure they meet performance, stability, and compliance standards.
* Create and execute deployment plans across various environments (staging, production), ensuring minimal risk and downtime.
* Collaborate closely with AI researchers, MLOps, and software engineers to understand release requirements, share feedback, and resolve pre-release issues.
* Identify and drive automation opportunities within the release pipeline to improve efficiency, reliability, and traceability.
* Oversee updates to infrastructure components, ensuring compatibility and performance across systems.
* Monitor deployments, proactively identify issues related to model behavior or infrastructure anomalies, and drive resolution with relevant teams.
* Maintain clear and accurate release documentation, including version history, deployment notes, and incident reports.