דרושים » הנדסה » Platform Engineer (AI Apps)

Requirements:
At least 4-5 years of experience in building ML/AI solutions, specifically in production environment
Strong experience in building and maintaining scalable machine learning infrastructures
Strong proficiency in Python
Solid understanding of Data Science and Machine Learning lifecycle and best practices for model deployment and serving
Excellent problem-solving abilities, coupled with a creative and strategic mindset
Extensive experience with ML frameworks
Understanding of microservice design and architecture
Proven ability to work effectively in a team setting

Requirements:
5+ years of experience building large-scale distributed systems or platforms, preferably in ML or data-intensive environments
Proficiency in Python with strong software engineering practices, familiarity with data structures and design patterns
Deep understanding of orchestration systems (e.g., Kubernetes, Airflow, Argo) and distributed computing frameworks (e.g., Ray, Spark, Dask) and
Experience with GPU compute infrastructure, containerization (Docker), and cloud-native architectures
Proven track record of delivering production-grade infrastructure or developer platforms.
Solid grasp of ML workflows, including model training, evaluation, and inference pipelines

Requirements:
2+ years of experience as an AI engineer or applied ML engineer working with LLMs (e.g. OpenAI, Claude, Mistral, etc.).
Proven experience with LangChain and/or LangGraph to build real-world AI agents or workflows.
Proficiency in Python/Type script, with hands-on experience in LLM frameworks and orchestration tools.
Experience designing and deploying autonomous agents, memory modules, and tool-using systems.
Familiarity with vector databases (FAISS, Pinecone, Weaviate, etc.) and knowledge retrieval systems.
Solid understanding of NLP workflows, prompt engineering, and tokenization strategies.
Experience with RESTful APIs and backend data integration.
Strong debugging, problem-solving, and architectural design skills.
Experience with Typescript, especially in AI agent or tool integration.
Prior experience deploying AI in production systems.
Familiarity with MLOps and CI/CD pipelines for AI-based applications.
Experience working with cloud platforms (AWS / GCP, or Azure).
MSc in Machine Learning, Computer Science, or a related technical field.

Requirements:
6+ years of experience as a Backend Developer, Data Engineer, or ML Engineer
Bachelors degree in Computer Science or a related STEM field
Strong proficiency in Python and ML tooling
Proven ability to build production-grade ML systems end-to-end
Deep experience with LLMs and ML frameworks (e.g., LangChain, LangGraph, Hugging Face, TensorFlow, PyTorch)
Solid foundation in system design, architecture, and microservice patterns
Excellent problem-solving skills and ownership mindset
Strong collaboration and communication abilities
Bonus if you have:
M.Sc. in Computer Science, Software Engineering, or similar field
Experience building and scaling LLM-powered applications
Familiarity with AWS and DevOps best practices (CI/CD, monitoring, IaC)
Exposure to NoSQL and real-time data processing pipelines.

Requirements:
5+ years of experience in AI/ML engineering or applied machine learning roles.
Proven experience designing, developing, and scaling production-grade AI systems (LLMs, conversational AI, or agent-based systems preferred), preferably based on known agents frameworks such as LangChain, LangGraph
Advanced proficiency in Python and experience writing clean, maintainable, and testable code.
Strong background in prompt engineering, LLM orchestration, and model evaluation.
Demonstrated ability to drive technical initiatives across disciplines (engineering, product, and data science).
Excellent communication skills with the ability to translate complex ideas into practical solutions.
Ability to work in cross-functional teams and communicate technical decisions clearly.

Requirements:
3+ years in Data Science, with industry experience in building and optimize AI/ML models
Specialized expertise in modern machine learning methods related to Natural Languages Processing (Transformers, Embeddings, LLMs)
Experience deploying AI/ML services and applications on at least one major cloud platform (Azure, AWS, GCP)
Pratrical skills using modern ML and Generative AI frameworks (Langchain, PyTorch, Tensorflow, Hugging Face and other open-source/ APIs and similar)
Experience with LLM applications and techniques, such as Retrieval-Augmented-Generation (RAG), Chane-of-Thought (CoT) prompting, and open-source LLMs fine tuning
Ability to write production ready code, with strong expertise in designing and implementing ML pipelines
Excellent problem-solving skills with a critical and creative mindset
Exceptional communication skills and a collaborative approach to teamwork
BSc, master or PhD in appropiate technology field (Computer Science, Electrical Engineering, Statistics, Applied Math, etc.)
A Big Plus If You Have:
Extensive experience in integrating and working with vector databases (e.g. Elastic, Milvus and more) for high-performance similarity search, semantic search and embeddings management.
Experience with building continuous integration and monitoring for Machine Learning applications.

Requirements:
What Experience Will Drive to Success
* Bachelors degree in data Science, Computer Science, Computer Engineering, or related field.
* 3+ years of hands-on software development experience.
* 1+ years of experience in AI engineering, with a proven track record of building and deploying AI systems.
* Strong proficiency in Python.
* Experience with AI frameworks and protocols such as LangChain, LangGraph, OpenAI, MCP, A2A, etc..
*  Solid understanding of LLM integration and prompt engineering for complex reasoning.
* Experience with Git and Jenkins.
* Strong problem-solving skills, a passion for innovation, and a self-driven mindset.
* Excellent communication and collaboration skills.
*  Hands-on experience with Big Data processing frameworks (Advantage).
*  data science experience or familiarity with applied Machine Learning (Advantage). What We Are Looking For Independent and Self-Driven: Were building an AI Department from the ground up, so we need someone proactive, takes ownership, and thrives in a startup-like environment. Passion for Innovation: Whether its building scalable data infrastructure or creating advanced AI models, we are looking for someone passionate about solving complex challenges and delivering innovative solutions. Collaboration Skills: we are all about teamwork. You will collaborate with engineers, data scientists, and product teams to build impactful solutions. Strong communication and interpersonal skills are key. Adaptability and Problem-Solving: Things move quickly at our company. You should be able to adapt to changing requirements and quickly solve problems in a fast-paced, dynamic environment. Can-Do Attitude: Were seeking motivated individuals who take the initiative, face challenges head-on, and can work independently while contributing to the team's success. About the Company: our mission is to protect every mobile app worldwide and its users. We provide mobile brands with the only patented, centralized, data -driven Mobile Cyber Defense Automation platform. Our platform delivers rapid no-code, no-SDK mobile app security, anti-fraud, anti-malware, anti-cheat, anti-bot implementations, configuration as code ease, Threat-Events threat-aware UI / UX control, ThreatScope Mobile XDR, and Certified Secure DevSecOps Certification in one integrated system. With us, mobile Developers, cyber and fraud teams can accelerate delivery, guarantee compliance, and leverage automation to build, TEST, release, and monitor the full range of cyber, anti-fraud, and other defenses needed in mobile apps from within mobile DevOps and CI/CD pipelines. Leading financial, healthcare, m-commerce, consumer, and B2B brands use our company to upgrade mobile DevSecOps and protect Android & IOS apps, mobile customers, and businesses globally. Today, our customers use our platform to secure over 50,000+ mobile apps, with protection for over 1 billion mobile end users projected.

Requirements:
B.Sc. in Computer Science or a related field.
5+ years of experience in backend software development, including system design and architecture.
Proficiency in at least one backend programming language (We write in Go).
Strong understanding of microservices architecture, RESTful APIs, and relational databases.
Deep familiarity with Kubernetes and the cloud-native ecosystem.
Demonstrated ability to tackle complex technical challenges and deliver high-quality solutions.
Ways to stand out from the crowd:
Expertise in Kubernetes internals and advanced cloud-native technologies.
Hands-on experience with HPC or AI/ML platforms.
Familiarity with AI inference workloads and performance optimization.
Proficiency in Linux, with knowledge in networking, security, storage, and virtualization.

Requirements:
Hands-on experience in generative AI or ML, with a strong track record of delivering real-world tools, prototypes, or research-backed systems.
Experience integrating AI solutions with APIs, data pipelines, and external systems in production environments.
Deep expertise in multimodal learning, generative models, or agent-based frameworksespecially involving LLMs.
Strong programming skills in Python and SQL, with hands-on experience in building and deploying AI/ML pipelines.
Understanding of cloud platforms (e.g., AWS, GCP, Azure) and AI infrastructure, including MLOps best practices.
Proven ability to integrate and operationalize AI-assisted development tools (e.g., GitHub Copilot, Cursor).
Excellent communication and collaboration skills, with the ability to translate technical advances into business impact.

Requirements:
BSc or MSc in Computer Science, Data Science, or related field (research experience is an advantage).
4+ years of experience in AI engineering, backend engineering, or similar.
Strong knowledge in one or more of deep learning, machine learning, NLP, or computer vision.
Proven experience building end-to-end AI workflows (e.g., semantic search, summarization, NER, question answering).
Proficiency in Python and software architecture design.
Ability to write clean, testable, production-grade code.
Strong problem-solving skills and ability to learn quickly.
Excellent communication skills in English, both verbal and written.