דרושים » הנדסה » Data Scientist AI Platform

Requirements:
MSc in Computer Science/Engineering or related field with a focus on applied statistics, AI, machine learning, or related fields - a must.
6+ years of production environment experience, working with predictive and probabilistic models, clustering algorithms, classification models, search and recommendation techniques - a must.
Proven record for successful production implementation of translating product/business requirements into a technical solution.
Team player, Ability to handle several tasks simultaneously.
Strong knowledge in Python, SQL, and NoSQL.
Spark and Big data technologies experience an advantage.
Research experience - an advantage.
Experience with e-commerce - an advantage.

Requirements:
MSc in Computer Science/Engineering or related field with a focus on applied statistics, AI, machine learning, or related fields - a must.
6+ years of production environment experience, working with predictive and probabilistic models, clustering algorithms, classification models, search and recommendation techniques - a must.
Proven record for successful production implementation of translating product/business requirements into a technical solution.
Team player, Ability to handle several tasks simultaneously.
Strong knowledge in Python, SQL, and NoSQL.
Spark and Big data technologies experience an advantage.
Research experience - an advantage.
Experience with e-commerce - an advantage.

Requirements:
8+ years of experience in data engineering, software engineering, or MLOps, with hands-on leadership in designing modern data platforms and distributed systems.
Proven experience implementing GenAI applications or infrastructure (e.g., building RAG pipelines, vector search, or custom LLM integrations).
Deep understanding of big data technologies (Kafka, Spark, Iceberg, Presto, Airflow) and cloud-native data stacks (e.g., AWS, GCP, or Azure).
Proficiency in Python and experience with GenAI frameworks like LangChain, LlamaIndex, or similar.
Familiarity with modern ML toolchains and model lifecycle management (e.g., MLflow, SageMaker, Vertex AI).
Experience deploying scalable and secure AI solutions with proper attention to privacy, hallucination risk, cost management, and model drift.
Ability to operate in ambiguity, lead complex projects across functions, and translate abstract goals into deliverable solutions.
Excellent communication and collaboration skills, with a passion for pushing boundaries in both data and AI domains.

Requirements:
4+ years of product management experience, ideally with a focus on AI/ML, NLP, or infrastructure/platform products.
Strong technical background in ML infrastructure, data systems, or engineering;
Experience building or scaling systems based on large language models or unstructured data processing.
Strong understanding of the AI/LLM ecosystem - including model deployment, evaluation, prompt orchestration, and data pipelines.
Proven track record of leading cross-functional teams to deliver technically complex products with measurable impact.
Excellent communication and collaboration skills, especially in technical environments.
Ability to operate in ambiguity.
Ability to drive clarity through structured thinking and sharp execution.
Background in ML infrastructure, AI products, or applied research is a plus.

Requirements:
7+ years of hands-on experience in machine learning and deep learning, with a strong focus on neural networks and NLP, including proven work on high-scale, production-level projects.
At least 3 years of proven experience in training, fine-tuning, and delivering LLMs and deep learning models at scale.
Advanced proficiency with Python and leading ML/DL frameworks: PyTorch, TensorFlow, Keras, etc.
Experience with MLOps, scalable model deployment, and optimizing inference for production environmnts.
Strong background in analyzing large, complex datasets using SQL, Spark, or similar tools.
Track record of delivering impactful AI solutions in real-world settings.
Experience in application/data security is a strong advantage.
Experience with cloud platforms (AWS, Azure, GCP) is a plus.
Experience with Kubernetes (K8s) and microservices infrastructure is a plus.
Experience with CI/CD practices and the full lifecycle of ML model development, from data preparation to deployment and monitoring, is a strong advantage.
Ph.D. or M.Sc. in Computer Science, Engineering, Math, or another quantitative/technical field is valued, but not required if you have strong relevant professional experience.
Excellent communication, mentoring, and collaboration skills.

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
Advantages:
Familiarity with distributed ML tools
Experience with real-time machine learning model deployment.
Familiarity with cybersecurity applications of machine learning
Advanced skills in performance optimization for high throughput systems
Tech Stack:
AWS (SageMaker, Lambda), PyTorch, vLLM, Ray, Hugging Face, Docker, Kubernetes, FastAPI, Flask.

Requirements:
Must:
BSc in Computer Science or related degree, or equivalent practical experience.
4+ years of hands-on experience as a software Engineer
Strong proficiency in Python and experience with AI/ML libraries (e.g., PyTorch, TensorFlow, Hugging Face).
Experience with AI APIs (OpenAI, Anthropic, Google AI, Microsoft AI) and integrating them into engineering workflows
Hands-on experience with developer productivity tools, AI-enhanced automation, and knowledge management systems
Strong problem-solving skills and ability to build AI-powered solutions that engineers love to use.
Nice to Have:
Deep understanding of LLMs, RAG (Retrieval-Augmented Generation), fine-tuning, and prompt engineering.
Experience with data pipelines, embeddings, and vector databases for AI-powered search and automation.
Experience working with cloud-based AI platforms and scalable AI architectures
Knowledge of containerization (Docker, Kubernetes) and CI/CD pipelines for deploying AI applications.
Experience with AI Ops, observability tools, and monitoring AI applications in production.
Familiarity with LangChain, AutoML, MLOps frameworks, and workflow automation tools.
A background in software engineering, DevOps, or developer tooling.
Strong entrepreneurial mindset, able to identify opportunities, move fast, and drive AI adoption in a high-impact environment.
Published research, open-source contributions, or side projects demonstrating innovative AI applications.
Why Join Us?
This role is for builders, problem-solvers, and AI enthusiasts who want to push the boundaries of engineering productivity with AI. Youll be at the forefront of AI-powered platform engineering, shaping how our company leverages AI to supercharge innovation, efficiency, and automation.
If youre looking to build game-changing AI platforms, experiment with cutting-edge tech, and make a real impactjoin us!

Requirements:
What Experience Will Drive to Success:
* Bachelors degree in data Science, Computer Science, Computer Engineering, or related field.
* 2+ years of experience in AI/ML engineering, with a proven track record of building and deploying ML & AI systems.
* Strong programming skills in Python and experience with ML frameworks such as TensorFlow, PyTorch, and Scikit-Learn.
* Hands-on experience with Big Data processing frameworks (Apache Spark, Dask, Ray).
* Hands-on experience with LLMs and AI Frameworks (LangChain, OpenAI, HuggingFace, LangGraph, etc.)
* Experience with Git and Jenkins.
* Strong problem-solving skills, a passion for innovation, and a self-driven mindset.
* Excellent communication and collaboration skills.
* Familiarity with AWS AI/ML services and cloud-based deployments (Advantage).
* Experience working with MLOps tools, including model registry, experiment tracking, and model serving (Advantage).
What We Are Looking For Independent and Self-Driven: Were building an AI Department from the ground up, so we need someone who is 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: Appdome is 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 Appdome. You should be able to adapt to changing requirements and quickly solve problems in a fast-paced, dynamic environment. Can-Do Attitude: Were looking for motivated individuals who take the initiative, face challenges head-on, and can work independently while contributing to team success. About the Company: Appdome's 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 Appdome, 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.

Requirements:
6+ years of hands-on experience in Machine Learning, Data Science, or related technical roles in the industry.
Entrepreneurial mindset: Excited about building something new and comfortable with the ambiguity of a startup-like environment within a larger company.
Proven track record of building and deploying machine learning models in production environments.
Technology leadership experience: History of making technical stack decisions and establishing development practices.
Strong proficiency in ML frameworks (TensorFlow, PyTorch, Scikit-learn) and programming languages (Python, SQL).
Experience with cloud platforms (AWS, GCP, Azure) and MLOps tools and practices.
Team building skills: Ability to establish technical culture and practices that will scale with team growth.
Excellent communication skills with ability to explain technical concepts to stakeholders and influence technical decisions.
Self-starter mentality: Comfortable taking ownership, making decisions, and driving initiatives independently.
Master's degree in Computer Science, AI, Data Science, Mathematics, or related quantitative field- PhD is a plus.
Experience in fintech or financial services is a plus.
Bonus: Experience being a founding engineer or early hire in a technical team.

Requirements:
M.Sc. or Ph.D. in Computer Science, Mathematics, Engineering or a related technical field.
5+ years of experience in NLP, machine learning or deep learning.
2+ years of experience managing ML/AI or software engineering teams
Excellent understanding of Deep Learning and modern NLP fundamentals, including Transformers, LLMs, RAG and Agents.
Hands-on experience with deep Learning frameworks (Pytorch, Tensorflow or JAX) and other relevant libraries (HuggingFace, vLLM, etc.).
Experience with LLM fine-tuning and deployment at scale on distributed GPU clusters.
Familiarity with STT / ASR models and common audio / speech processing methods.
Strong software engineering skills in Python.
Leadership
Proven ability to lead high-impact teams in a fast-paced applied research environment.
Experience defining and executing ambitious technical roadmaps and contributing to strategic technical decisions.
Passion for mentorship, team-building, and fostering a culture of innovation.
Strong cross-functional collaboration skills and the ability to work closely with PMs, engineers, and other stakeholders.