דרושים » תוכנה » Senior Machine Learning Engineer I - GenAI Applications

As a Machine Learning Engineer, you will work closely with experienced engineers and ML scientists to build scalable, production-grade GenAI applications. Your work will focus on designing, training, and deploying ML systems leveraging LLMs,, recommendation systems, and agent-based architectures, using state-of-the-art technologies. These solutions will directly power customer-facing experiences and play a key role in shaping the future of AI-driven travel products.

Key Job Responsibilities and Duties:

Deploying machine learning models: Design, develop and deploy in collaboration with scientists, scalable machine learning models and algorithms that provide content related insights and generative AI applications, ensuring scalability, efficiency, and accuracy.

Evaluating possible architecture solutions by taking into account cost, business requirements, emerging technologies, and technology requirements, like latency, throughput, and scale.

Generative AI Development: Contribute to the development of generative models such as GPT (Generative Pre-trained Transformer) variants or similar architectures for creative content generation, Q&A, chatbots, translation or other innovative applications.

Deployment and integration: Work closely with software engineers to integrate machine learning models into production systems. Ensure seamless deployment and efficient model inference in real-time environments. Collaborate with DevOps to implement effective monitoring and maintenance strategies.

Owning a service end to end by actively monitoring application health and performance, setting and monitoring relevant metrics and acting accordingly when violated.

Maintain clean, scalable code, ensuring reproducibility and easy integration of models into production environments, including CI/CD.

Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions.

As a Machine Learning Engineer, Youll work with top notch engineers and machine learning scientists on bringing it to the next level and enabling optimal customer experience while having a significant impact on the business. The work will focus on data and ML foundations for reusable training, optimization and deploying causal machine learning models.



Key Job Responsibilities and Duties:

Model evaluation and optimization: Conduct detailed model evaluation metrics and validation to ensure accuracy, reliability, and scalability. Optimize model performance by fine-tuning hyper parameters, feature engineering, and applying techniques such as ensemble learning and continuous learning.

Data pre-processing and analysis: Collaborate with data scientists and data engineers to collect, clean, pre-process, and transform large and wide datasets for model features and data monitoring. Conduct exploratory data analysis (EDA) to uncover insights and identify patterns that boost the model performance.

Building machine learning models: Design, develop and deploy in collaboration with scientists, scalable machine learning models and algorithms that provide personalized recommendations to users.

Deployment and integration: Work closely with software engineers to integrate machine learning models into production systems. Ensure seamless deployment and efficient model inference in real-time environments. Collaborate with DevOps to implement effective monitoring and maintenance strategies.

Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions. Provide technical guidance and mentorship to junior team members.

As a Senior Machine Learning Scientist, you will work closely with engineers and product to design, develop, evaluate and deploy Gen AI powered solutions for scalable, customer-facing applications. Your work will focus on applying state-of-the-art agentic capabilities and driving business impact based on rigorous evaluations and experimentation.



Key Job Responsibilities and Duties:

Design end-to-end agentic systems, delivering high-quality, performant, and efficient solutions to production

Build agentic solutions for different tasks and use cases using state-of-the-art techniques

Develop and carry out evaluation strategies, including formulating new metrics and building evaluation judges

Pioneer and promote best practices and adoption of new technology in GenAI application development

Lead and mentor other team members, providing technical guidance and timely feedback to develop the team and motivate them to achieve their goals.

Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into ML solutions.

Conduct deep data analysis to evaluate model performance, label quality, features exploration.

Work closely with ML engineers to ensure and improve the solutions latency/throughput meets product requirements and ensure deployment of your model to production.

Join our companys AI research group, a cross-functional team of ML engineers, researchers and security experts building the next generation of AI-powered security capabilities. Our mission is to leverage large language models to understand code, configuration, and human language at scale, and to turn this understanding into security AI capabilities which will drive our company AI future security solutions.
We foster a hands-on, research-driven culture where youll work with large-scale data, modern ML infrastructure, and a global product footprint that impacts over 100,000 organizations worldwide.
Key Responsibilities
As a Senior ML Research Engineer, you will be responsible for the end-to-end lifecycle of large language models: from data definition and curation, through training and evaluation, to providing robust models that can be consumed by product and platform teams.
Own training and fine-tuning of LLMs / seq2seq models: Design and execute training pipelines for transformer-based models (encoder-decoder, decoder-only, retrievalaugmented, etc.), and fine-tune open-source LLMs on our company-specific data (security content, logs, incidents, customer interactions).
Apply advanced LLM training techniques such as instruction tuning, preference / contrastive learning, LoRA / PEFT, continual pre-training, and domain adaptation where appropriate.
Work deeply with data: define data strategies with product, research and domain experts; build and maintain data pipelines for collecting, cleaning, de-duplicating and labeling large-scale text, code and semi-structured data; and design synthetic data generation and augmentation pipelines.
Build robust evaluation and experimentation frameworks: define offline metrics for LLM quality (task-specific accuracy, calibration, hallucination rate, safety, latency and cost); implement automated evaluation suites (benchmarks, regression tests, redteaming scenarios); and track model performance over time.
Scale training and inference: use distributed training frameworks (e.g. DeepSpeed, FSDP, tensor/pipeline parallelism) to efficiently train models on multi-GPU / multi-node clusters, and optimize inference performance and cost with techniques such as quantization, distillation and caching.
Collaborate closely with security researchers and data engineers to turn domain knowledge and threat intelligence into high-value training and evaluation data, and to expose your models through well-defined interfaces to downstream product and platform teams.

we are looking for a Data science Team Lead.
As the Data Science Team Lead, you will lead a talented team of data scientists and ML engineers building the infrastructure, systems, and workflows for designing, training, evaluating, and deploying machine learning models that protect millions of users worldwide from fraud and account compromise.
This role combines hands-on technical leadership with people management and strategic ownership. You will drive innovation across real-time model serving, customer-specific model tuning, offline AI evaluations, and scalable ML systems in a production-grade SaaS environment.
If you are passionate about applied machine learning, fraud detection, and building intelligent systems at scale - we want you on our team.
What youl do:
Lead and mentor a team of Data Scientists and ML Engineers focused on fraud detection and response capabilities.
Build ML infrastructure focused on design, train, evaluate, and optimize machine learning models for real-time fraud prevention and risk assessment.
Own the lifecycle of ML models in production, including experimentation, deployment, monitoring, retraining, and performance optimization.
Drive customer-specific model training and tuning strategies to improve accuracy and adaptability across different customer environments.
Build and improve offline AI evaluation frameworks to measure model quality, drift, effectiveness, and business impact.
Collaborate closely with Engineering, Product, Security, and Data teams to deliver scalable and reliable AI-powered capabilities.
Define best practices for model serving, feature engineering, experimentation, observability, and operational excellence.
Balance model performance, latency, scalability, explainability, and operational constraints in high-scale production environments.
Promote a culture of technical excellence, continuous improvement, ownership, and innovation.

We are always looking for exceptional talent to join us on the journey!
We are always looking for exceptional talent to join us on the journey!


Your Mission

As an MLOps Engineer at Nuvei, 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.

As a Machine Learning Scientist, you will design, build, and deploy advanced models that guide pricing and promotional optimization across us. You will work closely with other scientists, engineers, analysts, and product teams to translate complex business challenges into scalable, data-driven solutions that deliver measurable impact.

Key Job Responsibilities and Duties:

Develop and deploy models for causal inference, uplift estimation, and optimization to measure and maximize the incremental effect of price and promotion decisions.

Design and improve dynamic pricing algorithms that balance competitiveness, conversion, and profitability.

Contribute to the development of platform capabilities, enhancing experimentation, simulation, and decision-support capabilities.

Partner with product and business stakeholders to translate scientific insights into actionable strategies.

Stay up to date with the latest advances in machine learning, causal modeling, and pricing optimization, and apply them pragmatically at scale.

Were looking for a Senior Data Science who is excited about designing and building production-grade AI systems powered by modern LLMs and machine learning.
This role is ideal for someone who enjoys working at the intersection of AI, engineering, and product, and who is passionate about turning cutting-edge AI capabilities into reliable, scalable systems that solve real customer problems.
Youll work closely with product managers, data scientists, and engineers to design, build, and deploy AI-powered solutions - including LLM pipelines, agents, and intelligent automation systems that power our core products.
This is a hands-on role where youll take ownership of the full lifecycle of AI features - from problem framing and architecture design to deployment, evaluation, and iteration in production.
Responsibilities:
Design and build AI-powered systems that leverage LLMs, embeddings, and modern NLP techniques to transform raw product data into structured, actionable insights
Develop and maintain production-grade AI pipelines including prompt workflows, agents, retrieval systems (RAG), and automated decision processes
Work closely with product and engineering teams to translate business needs into scalable AI solutions
Architect systems that combine LLMs, data pipelines, and traditional ML into robust end-to-end products
Experiment with and integrate new AI tools, models, and frameworks to continuously improve system capabilities and performance
Own the full lifecycle of AI features - from design and prototyping to deployment, monitoring, and iteration
Ensure reliability and performance of AI systems in production, including evaluation frameworks, guardrails, and monitoring
Collaborate across teams to define best practices for AI system design, prompt engineering, and agent orchestration
Collaborate closely with cross-functional team members, effectively communicate complex ideas, share knowledge, and mentor engineers and data scientists to elevate team standards and impact

At our company, we redefine cyber defense vision by combining AI and human expertise to create products that protect nations and critical infrastructure. This is more than a job; its a Dream job. we are where we tackle real-world challenges, redefine AI and security, and make the digital world safer. Lets build something extraordinary together.
our company's AI cybersecurity platform applies a new, out-of-the-ordinary, multi-layered approach, covering endless and evolving security challenges across the entire infrastructure of the most critical and sensitive networks. Built as part of a broader sovereign AI platform, our technology is designed to operate in on-premise, private cloud, and air-gapped environments, enabling nations to maintain full control over their data, infrastructure, and AI capabilities. Central to our company's proprietary Cyber Language Models are innovative technologies that provide contextual intelligence for the future of cybersecurity.
At our company, our talented team, driven by passion, expertise, and innovative minds, inspires us daily. We are not just dreamers, we are dream-makers.
Responsibilities
Build and operate ML training infrastructure - distributed training pipelines, compute scheduling, and reproducible experiment workflows that data scientists rely on daily.
Own model serving and inference systems - packaging, deployment, autoscaling, A/B testing, canary rollouts, and latency/cost optimization for production models.
Run feature stores, model registries, and dataset versioning - enabling self-serve feature engineering, model lineage, and reproducible experiments across teams.
Build experiment tracking and evaluation infrastructure - automated evals, comparison dashboards, drift detection, and monitoring that give teams visibility into model behavior and performance.
Build and maintain production pipelines for training, fine-tuning workflows, and serving domain models - owning reliability, reproducibility, and scale.
Build and maintain the monitoring and observability layer - model performance tracking, data and prediction drift detection, data quality validation, and alerting.
Improve performance and cost across the ML stack - training throughput, inference latency, batch vs. real-time tradeoffs, and compute cost management.
Ship shared tooling - libraries, templates, CI/CD for models, IaC, and runbooks - while collaborating across Data Platform, AI, Data Science, Engineering, and DevOps. Own architecture, documentation, and operations end-to-end.