דרושים » תוכנה » Senior Data Scientist and LLM Engineer

Requirements:
MSc or PhD in a computational or statistical field (Computer Science, Statistics, Applied Math, Econometrics, Operations Research), or equivalent experience
3+ years Data Science experience.
Experience working on one of the cloud platforms: AWS, GCP, Azure
Well versed in Data Science languages, tools and frameworks, including data processing platforms and distributed computing systems (for example Python, R, SQL, SKLearn, NumPy, Pandas, TensorFlow, Keras)
Familiarity with LLM based applications an advantage
Familiarity with LLM security and safety aspects - an advantage
Resourceful, motivated, and organized.

Requirements:
MSc in Computer Science, Engineering, or a related field
Focus on AI, ML, or DS.
5+ years of experience in DS roles, with a strong emphasis on building and deploying machine learning models in production environments.
Expertise in Python and SQL.
Experience with machine learning frameworks such as TensorFlow or PyTorch.
Familiarity with cloud platforms (e.g., AWS, GCP) and containerization tools (e.g., Docker, Kubernetes).
Proven experience in developing, fine-tuning, and deploying ML models, particularly in NLP.
Ability to translate complex business problems into data-driven solutions.
Excellent verbal and written communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.

Requirements:
BSc or MSc in Computer Science, Software Engineering, Data Science, or a related field.
3+ years of experience as a Data Scientist or ML Engineer.
Hands-on expertise with machine learning techniques: regression, classification, clustering, time series
Deep interest and foundational knowledge, including hands-on experience with:
Transfer learning & fine-tuning adapting large pre-trained models (like GPT, LLaMA, Mistral) to domain-specific tasks.
LLMs, Prompt Engineering, and Chain-of-thought (CoT) prompting
Designing MCP APIs, Agent,s and Multi-Agent Orchestration
Vector databases (RAG) and GraphRAG-based solutions
Models for Content Moderation
Familiarity with MLOps & LLMOps practices automating model training, testing, deployment, monitoring, and governance.
Strong proficiency in Python (or other relevant programming languages).
Excellent written and verbal communication skills in English.

Requirements:
* MSc in Computer Science, Engineering, or a related field
* Focus on AI, ML, or DS.
* 5+ years of experience in DS roles, with a strong emphasis on building and deploying machine learning models in production environments.
* Expertise in Python and SQL.
* Experience with machine learning frameworks such as TensorFlow or PyTorch.
* Familiarity with cloud platforms (e.g., AWS, GCP) and containerization tools (e.g., Docker, Kubernetes).
* Proven experience in developing, fine-tuning, and deploying ML models, particularly in NLP.
* Ability to translate complex business problems into data-driven solutions.
* Excellent verbal and written communication skills, with the ability to convey complex technical concepts to non-technical stakeholders. Preferred Qualifications:
* Background in academic or industrial research in AI.
* Familiarity with the e-commerce domain and related data challenges.
* Previous experience leading projects in a DS capacity.

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:
M.Sc. in Computer Science or a related field. Ph.D. an advantage.
4+ years of hands-on industry experience in NLP, deep learning, large language models, generative AI, and prompt engineering.
Proficiency in Python a must.
Proven track record of designing and operating AI/ML systems in production, including considerations for performance, observability and monitoring a must.
Hands-on expertise with modern ML frameworks (PyTorch, Hugging Face Transformers) and GenAI toolkits (e.g., LangChain, LlamaIndex); proficiency with cloud ML platforms (AWS, GCP, Azure) and MLOps best practices.
Experience with ASR an advantage.
Team player.

Requirements:
Requirements
BSc in Computer Science, Engineering, Mathematics, Physics, or a related quantitative field. MSc is an advantage.
3+ years of hands-on experience in NLP roles with demonstrated production deployment of ML models.
At least 1 year of experience with LLMs (e.g., OpenAI, HuggingFace, Cohere, Anthropic, or similar).
Strong programming skills in Python with practical use of ML and NLP frameworks (e.g., HuggingFace Transformers, spaCy, scikit-learn).
Familiarity with prompt engineering principles, including prompt tuning, template construction, and response evaluation.
Experience with model monitoring and optimization in real-world settings.
Understanding of model limitations, prompt sensitivity, and output consistency.

Advantages:
Knowledge of ML-Ops practices and model deployment pipelines.
Experience with LLM safety, bias mitigation, or prompt hardening strategies.
Exposure to multilingual or multimodal prompting.
Familiarity with compliance frameworks in healthcare or other regulated environments (e.g., HIPAA, SOC2).

Requirements:
Minimum of 5 years of experience as a Back End developer.
Deep knowledge of Generative AI principles, including Large Language Models (LLMs) and RAG techniques and, modern deployment architectures.
Ability to lead team to design services, modules, APIs, and folder structures that enable REST API communication and facilitate a plug-and-play approach for various agent pods (compatible with langgraph, pydanticAI, etc.)
Practical Implementation: Hands-on experience in implementing and integrating new ideas and tools within the GenAI landscape.
Programming and Tools: Proficiency with Python, PySpark, Langchain, LlamaIndex, RAG, ChromaDB, QDrant, Weaviate, Neo4j, Knowledge Graphs, AWS Bedrock, GCP Vertex, Azure OpenAI, Git/GitHub, Docker, SQL, FastAPI, PyTorch/TensorFlow, and Pydantic.
Cloud Platforms: Experience with managed cloud services provided by hyperscalers such as AWS, Azure, or GCP.
Excellent problem-solving and analytical skills. Degree in Computer Science, Artificial Intelligence, Machine Learning or a related field.
Advanced level of English.
Working Israeli permit- a must
B.A/M.Sc. in Data Science, Computer Science, Mathematics, Statistics, etc- an advantage

Requirements:
Strong NLP and LLM Knowledge: A foundational understanding of NLP techniques and a keen interest in LLM technologies. Coursework or projects in this area are highly desirable.
Passion for Emerging AI Technologies: A demonstrated interest in the
advancements in NLP, LLMs, generative AI, machine learning, and deep learning.
A desire to stay current with the latest developments in transformer architectures, self-supervised learning, and model fine-tuning is essential.
Robust Technical Expertise in Data Science and LLMs: A solid understanding of the data science principles that underlie LLMs, including tokenization, embeddings, pre-training and fine-tuning methods, data augmentation, and prompt engineering.
Global Collaboration: The ability to collaborate effectively with cross-functional teams and partners in a global setting to contribute to complex, LLM-focused projects.
Adaptability and Eagerness to Learn: A quick learner with the flexibility to thrive in a fast-paced, innovation-driven environment and adapt to evolving LLM techniques and tools.
Exceptional Communication Skills: Strong verbal and written communication skills, with the ability to participate in discussions and explain AI concepts to both technical and non-technical audiences in a clear manner.
Project and Stakeholder Engagement: An interest in learning how to manage complex projects, align with multiple stakeholders, and contribute to data-driven initiatives.
Relavent M.Sc. /MA / Phd
Advantages:
Familiarity with end-to-end AI projects (from inception to production). We primarily
use Python in all stages of development.
Comfortable working in a Linux environment.
Exposure to building end-to-end reusable pipelines, from data acquisition to model output delivery.

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.