דרושים » AI » AI Solutions Expert

Requirements:
Technical Expertise:
Programming: Advanced proficiency in Python for backend development and AI applications
GenAI Mastery: Deep understanding of large language models (LLMs) and experience with major model APIs (OpenAI, Anthropic, Google, etc.)
Multi-Agent Systems: Expertise in designing and implementing GenAI multi-agent architectures
Prompt Engineering: Advanced skills in prompt design, optimization, and engineering techniques
Cloud Platforms:
Required: Hands-on experience with AI services in at least one major cloud platform (GCP, Azure, or AWS)
Advantage: Experience across multiple cloud platforms (AI Search, Vertex AI, SageMaker, etc.)
Development Frameworks: Experience with GenAI frameworks like LangChain and cloud-based retrieval services
Software Engineering: Strong background in microservices architecture, API development, and production system design
AI/ML Fundamentals: Solid understanding of deep learning principles and GenAI techniques
Containerization (Advantage): Experience with Docker and Kubernetes for deployment and orchestration
OCR Technologies (Advantage): Experience with Optical Character Recognition systems and document processing
Data Pipelines (Advantage): Experience building and maintaining data processing pipelines
Professional Experience
Mid+ Level Experience: 2+ years in AI/ML development with significant GenAI project experience
Production Systems: Proven track record of deploying and maintaining AI solutions in production environments
Client-Facing Experience: Comfortable with technical presentations and requirement gathering sessions
Education & Background
Preferred: Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or related technical field
Alternative: Demonstrated industrial experience in developing deep learning and GenAI solutions (degree not required with strong portfolio)
Soft Skills
Problem-Solving: Excellent analytical and creative problem-solving abilities
Communication: Strong technical communication skills for both technical and non-technical audiences
Collaboration: Proven ability to work effectively in cross-functional teams
Adaptability: Thrives in fast-paced environments and eager to learn emerging technologies
Consulting Mindset: Ability to understand client needs and provide strategic technical guidance.

Requirements:
2-4 years of experience as a Business Applications/Web Developer, IT/System Engineer, Automation Engineer, or AI-focused Solution Engineer, or equivalent hands-on project experience.
Practical experience working with LLMs, AI agents, and RAG-based solutions, and connecting them to real-world business workflows.
Strong experience with APIs, webhooks, and system integrations.
Hands-on experience with JavaScript for building and extending applications or integrations (Python is a plus).
Experience working with low-code/no-code automation tools such as n8n, Workato, Make, or similar platforms.
Curiosity and motivation to create AI-driven helpers and automation experiences that streamline how employees work and interact with internal systems.
Strong problem-solving and self-learning abilities, with a hands-on approach to tackling complex challenges and delivering real solutions.
Strong communication skills, with the ability to clearly convey technical ideas and solutions to a wide range of audiences.

Requirements:
Extensive hands-on experience in Business Applications and/or software development
Experience building end-to-end AI solutions and automations
Deep understanding of business processes and a drive to impact business results
Experience with AI platforms (e.g., OpenAI, Claude, Gemini) and automation/orchestration tools (e.g., n8n, Make.com, LangChain)
Development capabilities, preferably in Python
Hands-on experience designing and consuming REST APIs, working with HTTP methods, authentication mechanisms, and integrating GTM platforms into AI-driven workflows.
High degree of independence and initiative - able to run projects autonomously
Sharp analytical skills and a structured problem-solving mindset
Excellent written and verbal communication skills; able to bridge technical and non-technical stakeholders
Passion for continuous learning, experimentation, and staying current with AI trends
Experience working with GTM or RevOps teams in a SAAS company - a big advantage
Experience with Salesforce - a big advantage.

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 frameworks-especially 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).
Previous experience in leading or mentoring AI/ML teams, fostering collaboration and technical excellence.
Excellent communication and collaboration skills, with the ability to translate technical advances into business impact.

Requirements:
Overall of 5 years of hands-on experience (3 years automation, 2 years in GenAI)
Hands-on experience building AI-powered workflows or agents
Strong understanding of LLMs and prompt engineering
Experience with API-based integrations and SaaS systems
Familiarity with automation platforms and orchestration tools
Experience with agentic patterns (planner-executor, tool-using agents, multi-agent setups)
Experience implementing human-in-the-loop and fallback mechanisms
Understanding of AI limitations, bias, and reliability concerns
Strong problem-solving and systems-thinking mindset
Ability to work independently and drive initiatives end-to-end
Excellent communication with both technical and non-technical stakeholders.

Requirements:
8+ years of fullstack development experience with strong skills in TypeScript/JavaScript, React, and Python (or Node/Go for backend).
Solid understanding of LLM APIs, agent frameworks (e.g., LangChain, AutoGPT, CrewAI), or custom AI pipelines- Advantage
Experience with modern cloud infrastructure (e.g., AWS, GCP, Docker, CI/CD).
Familiarity with vector databases (e.g., Pinecone, Weaviate, FAISS) and retrieval-augmented generation (RAG)- Advantage
Product-oriented mindset: you care deeply about building things that work well for users.
Bonus: experience with observability, feedback loops for AI agents, or embedded AI evaluation techniques.

Requirements:
Backend-LLM & agent architecture - 5+ years in production ML/AI and backend systems; recent hands-on experience with backend LLM systems, including agent workflows (e.g., LangGraph or similar), LangChain tooling and chains, state management, and streaming (e.g., SSE). You think in terms of nodes, state schemas, routing, and human-in-the-loop.
Technical stack - Proficient in Python; comfortable with LangGraph, LangChain, FastAPI, PostgreSQL, and optionally Azure AI Search or similar. Experience with LLM providers (OpenAI/Azure, Google Vertex AI, etc.) and RAG (retrievers, chunking, reranking) expected.
Generative AI in production - Proven track record building production GenAI applications, including multi-step agents, RAG, tool-augmented LLMs, and ideally human-in-the-loop or review flows. You care about observability, validation, and safe rollout.
Bachelor's degree or higher in Computer Science or a related field, and strong communication and collaboration skills.

Requirements:
Requirements:

3+ years of hands-on experience in software engineering, with at least 1-2 years working on generative AI projects (LLMs, diffusion models, multimodal models, etc.).

Proven ability to go from idea to production-ideally with examples of real-world AI features youve shipped.

Fluency in Python, Node.js, or similar languages used in ML and full-stack development.

Experience with prompt engineering, fine-tuning, or embedding models using frameworks like LangChain, LlamaIndex, or similar.

Familiarity with AWS services and best practices, including Lambda, S3, SageMaker, ECS/EKS, Bedrock etc.

Experience with MLOps and model deployment practices (e.g., containerization, GPU inference, vector databases).

Creativity and initiative-able to pitch and prototype ideas with minimal oversight.

Strong communication skills and the ability to explain technical concepts to non-technical stakeholders.

Nice-to-Haves:

Prior experience integrating generative AI in FinOps or cloud cost optimization tools.

Background in NLP, computer vision, or other relevant ML fields.

Contributions to open-source AI tools or research.

Knowledge of responsible AI principles and handling model risks.

Requirements:
7+ years of experience in data infra or backend engineering.
Strong knowledge of data services architecture, and ML Ops.
Experience with cloud-based data infrastructure in the cloud, such as AWS, GCP, or Azure.
Deep experience with SQL and NoSQL databases.
Experience with Data Warehouse technologies such as Snowflake and Databricks.
Proficiency in backend programming languages like Python, NodeJS, or an equivalent.
Proven leadership experience, including mentoring engineers and driving technical initiatives.
Strong communication, collaboration, and stakeholder management skills.
Bonus Points:
Experience leading teams working with serverless technologies like AWS Lambda.
Hands-on experience with TypeScript in backend environments.
Familiarity with Large Language Models (LLMs) and AI infrastructure.
Experience building infrastructure for Data Science and Machine Learning.
Experience collaborating with BI developers and analysts to drive business value.
Expertise in administering and managing Databricks clusters.
Experience with streaming technologies such as Amazon Kinesis and Apache Kafka.

Requirements:
8+ years of software engineering experience, with at least 2+ years in AI/ML platforms or intelligent automation.
Strong background in distributed systems, APIs, microservices, container orchestration (ECS/EKS), and cloud platforms (AWS/GCP/Azure)
Proven experience building and deploying LLM-based applications using platforms such as OpenAI, Anthropic, AWS Bedrock, or similar
Solid understanding of RAG pipelines, vector databases, embeddings, and retrieval techniques (chunking, indexing, filtering, relevance tuning)
Ability to thrive in ambiguous problem spaces, take ownership, and move quickly in a startup-like environment
Strong problem-solving and analytical skills, with a product- and impact-driven mindset
Experience collaborating across teams and leading technical projects end-to-end
Bachelors degree in Computer Science, AI, or a related field (or equivalent practical experience)