דרושים » תוכנה » MCP (Model Context Protocol) Software Engineer

Requirements:
Experience: 8+ years of backend engineering experience, with at least 2 years in an engineering leadership role (Tech Lead, Staff Engineer, or Engineering Manager). Strong proficiency in Go and Python.

People Leadership: Demonstrated ability to hire, mentor, and develop engineers at varying levels. Comfortable balancing technical contribution with team management responsibilities.

AI/LLM Experience: Prior experience building workflows powered by LLMs, RAG, or autonomous agents. Strong understanding of agent frameworks and key components including model integration, tool calling patterns, and Model Context Protocol (MCP).

Cloud Expertise: Deep knowledge of at least two major cloud providers (AWS, Azure, or GCP).

Systems Engineering: Strong understanding of distributed systems, scalability, concurrency, and resilient architecture.

Data Proficiency: Solid experience with data modeling, RDBMS (SQL), and distributed caching solutions like Redis.

Education: BS/MS in Computer Science or equivalent professional experience in data structures and algorithms.

Requirements:
Experience: 8+ years of backend engineering experience, with at least 2 years in an engineering leadership role (Tech Lead, Staff Engineer, or Engineering Manager). Strong proficiency in Go and Python.

People Leadership: Demonstrated ability to hire, mentor, and develop engineers at varying levels. Comfortable balancing technical contribution with team management responsibilities.

AI/LLM Experience: Prior experience building workflows powered by LLMs, RAG, or autonomous agents. Strong understanding of agent frameworks and key components including model integration, tool calling patterns, and Model Context Protocol (MCP).

Cloud Expertise: Deep knowledge of at least two major cloud providers (AWS, Azure, or GCP).

Systems Engineering: Strong understanding of distributed systems, scalability, concurrency, and resilient architecture.

Data Proficiency: Solid experience with data modeling, RDBMS (SQL), and distributed caching solutions like Redis.

Education: BS/MS in Computer Science or equivalent professional experience in data structures and algorithms.

Requirements:
What we need to see:

BSc/MSc in CS/CE or related field (or equivalent experience).

Strong background in AI with experience deploying and monitoring AI/ML models, LLMs and agents to production systems at scale, including distributed and multi-node environments - at least 5 years of experience.

Proficiency in programming languages such as Python, Java, or Scala, along with experience in using ML/AI frameworks and libraries (e.g. TensorFlow, PyTorch).

Proficiency in microservices architecture, container orchestration, cloud platforms, and scalable infrastructure for training and inference workloads.

Knowledge of inference optimization techniques.

Understanding of build infrastructure and CI/CD tools and practices (e.g. GitLab, GitHub Actions, Jenkins).

You are detail-oriented and care deeply about robust, well tested, high-performance code in production environments.

You are proactive, take full ownership of your deliverables, have a can-do approach, and excellent communication and collaboration skills, able to work effectively in multifunctional teams.

Ways to stand out from the crowd:

Knowledge of network protocols and Linux internals.

Security and networking background, with knowledge of security protocols, network architectures, firewalls, intrusion detection systems, and other relevant security and networking concepts.

Experience deploying and optimizing generative models and agents.

Knowledge of network security principles and practices.

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:
Experience: 3+ years of full-stack development experience, preferably in AI/ML domains
AI Development Tools: Hands-on experience with AI-assisted coding environments, IDEs, and agent-based workflows (e.g., Cursor, Windsurf, GitHub Copilot, Codex, Claude Code, and similar platforms)
Model Knowledge: Deep understanding of different AI model capabilities, limitations, and appropriate use cases (GPT-5, Claude, Gemini, etc.)
Frontend Development: Expertise in React, TypeScript and modern web development practices
Backend Development: Strong proficiency in Java with frameworks like Dropwizard
Database & Infrastructure: Knowledge of scalable database design and containerization
Observability Tools: Experience with tracing, monitoring, and evaluation systems
Collaboration: Strong communication skills for working in a distributed, global team environment
Problem Solving: Ability to work in ambiguous environments and solve complex technical challenges
Nice to have:
Open Source: Experience contributing to or maintaining open-source projects
AI/ML Experience: Understanding of Large Language Models, RAG systems, and AI application architectures

Requirements:
Technical Expertise:
Advanced proficiency in Python for backend development and AI systems
Deep understanding of large language models and generative AI techniques
Hands-on experience designing and implementing multi-agent architectures
Advanced prompt engineering and orchestration strategies
Strong background in microservices architecture, API development, and production system design
Hands-on experience with at least one major cloud platform (GCP, Azure, or AWS)
Professional Experience
3-4+ years of experience in AI/ML development with significant GenAI project exposure
Proven experience in end-to-end software development in Python
Proven experience deploying and maintaining AI systems in production
Client-facing experience in technical consulting or solution delivery roles
Advantages:
Hands-on experience developing directly against LLM provider SDKs and APIs (e.g., OpenAI, Anthropic, Google), including tool/function calling, streaming, and advanced orchestration patterns
Docker and Kubernetes experience
OCR systems and document intelligence experience
Data pipeline development and maintenance experience
Education & Background:
Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or related field (or equivalent demonstrated industry experience)
Soft Skills:
Strong problem-solving and analytical capabilities
Excellent technical communication skills
Ability to collaborate effectively across teams
Adaptability in fast-paced, evolving technical environments
Consulting mindset with strong client focus

Requirements:
5+ years of professional software development experience
Strong hands-on expertise with server-side languages such as Golang, Java, Node.js, or similar backend technologies
Practical experience building scalable, cloud-native microservices in a SaaS environment
Solid understanding of Docker, Kubernetes, messaging systems, caching, and other core cloud-native components
Familiarity with AI technologies, including MCP, AI agents, and LLMs - advantage
Hands-on experience using AI coding agents such as Cursor, GitHub Copilot, Claude, or similar tools - advantage
Familiarity with modern CI/CD practices, DevOps workflows - advantage

Requirements:
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:
3+ years as a Data or Software Engineer in cloud-native, high-scale environments
Strong Proficiency in Python and Microservices architecture
Expertise in Spark and distributed data processing at scale
Deep Knowledge of AWS ecosystem (S3, Glue, RDS, EMR)
Strong SQL skills and experience optimizing complex query executions
Proven Experience with data modeling for high-scale, large volume data sets
Experience with modern orchestration tools like Airflow
Experience with Real-time streaming (Kafka, Kinesis, or Flink)
Experience supporting ML/LLM pipelines and Vector Databases (Advantage)
Experience with Databricks or SingleStore (Advantage)

Requirements:
Minimum Qualifications
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
Demonstrated experience driving change within an organization and leading complex technical projects
Experience utilizing data and analysis to explain technical problems and provide detailed feedback and solutions
Programming experience in a relevant language
Preferred Qualifications
Understanding of information retrieval concepts, such as indexing, querying, and ranking
Masters degree or PhD in Computer Science or a related technical field
Knowledge of NLP techniques, including text preprocessing, tokenization, and sentiment analysis
Demonstrated experience with data structures and algorithms, including graph theory and optimization techniques
Experience with frameworks like TensorFlow, PyTorch, or Scikit-learn
Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies.