AI/ML Specialist

Artificial Intelligence Jobs Opportunity

Building and Maintaining Hierarchical/Hybrid Multi-Agent Reinforcement Learning (MARL) System with Agent Orchestration and RAG Architecture

Job Title: AI Developer, AI/ML/LLM Specialist – Graduate Python Developer

Job Description: Building and Maintaining Hierarchical/Hybrid Multi-Agent Reinforcement Learning

(MARL) System with Agent Orchestration and RAG Architecture using Python and

LangGraph of LangChain and other tools

Job Type: Full-Time

Location: Advanced Analytics, Riyadh, Saudi Arabia

About Us

Advanced Analytics is a leading innovator in Decision Support Systems using artificial intelligence and multi-agent frameworks, dedicated to solving complex challenges through cutting-edge technology. We are seeking a talented and driven new graduate to join our team as a Python Developer to design and implement a Hierarchical/Hybrid Multi-Agent Reinforcement Learning (MARL) System with Agent Orchestration and RAG (Retrieval-Augmented Generation) Architecture.

This is an exciting opportunity to work on state-of-the-art AI projects that push the boundaries of what’s possible in multi-agent systems and knowledge-driven workflows.

Key Responsibilities

Develop Hierarchical/Hybrid MARL Systems:

• Design and implement multi-agent reinforcement learning (MARL) systems that utilize hierarchical or hybrid architectures.
• Apply advanced RL techniques, such as Centralized Training with Decentralized Execution (CTDE), MADDPG, and QMIX, to enable agent collaboration and coordination.

Integrate RAG Pipelines into Agent Workflows:

• Build Retrieval-Augmented Generation (RAG) pipelines to optimize information retrieval and decision-making for agents.
• Leverage vector databases (e.g., Pinecone, FAISS, or Weaviate) and embedding techniques for context-aware retrieval.

Design Agent Orchestration Frameworks:

• Use LangGraph to create dynamic workflows for agent orchestration, where agents can invoke other agents, external tools, or APIs.
• Develop hierarchical agent structures with task-managing agents orchestrating sub-agents for specific problem domains.

Optimize System Performance and Scalability:

• Build scalable multi-agent systems capable of handling high-volume tasks and large-scale data.
• Optimize performance in distributed and cloud-based environments.

Collaborate and Experiment:

• Work closely with AI researchers, engineers, and developers to define system requirements and architectures.
• Conduct experiments to evaluate and iterate on MARL, RAG, and agent orchestration designs.

Write Clean, Modular Python Code:

• Develop maintainable and reusable code using best practices in Python development, including asynchronous programming, modular design, and version control.

Required Skills and Qualifications

Essential:

• Education: Bachelor’s degree in Computer Science, Artificial Intelligence, Software Engineering, or a related field.
• Programming Skills: Advanced Python skills, including experience with libraries such as NumPy, pandas, and scikit-learn.
• Multi-Agent Systems: Understanding of multi-agent systems, including hierarchical architectures and agent coordination.
• Reinforcement Learning (RL):
• Familiarity with RL algorithms such as Q-learning, DDPG, and Actor-Critic methods.
• Knowledge of MARL frameworks like CTDE, MADDPG, QMIX, or QTRAN.
• Retrieval-Augmented Generation (RAG):
• Experience with RAG pipelines for information retrieval and response generation.
• Familiarity with vector similarity search tools (e.g., Pinecone, FAISS, or Weaviate).
• LangGraph/Agent Orchestration: Knowledge of LangGraph or similar agent orchestration frameworks for building workflows.
• Problem-Solving: Strong analytical and debugging skills to tackle complex AI challenges.
• Team Collaboration: Excellent communication skills to work effectively in cross-functional teams.

Preferred:

• NLP and RAG Integration: Experience with NLP frameworks like Hugging Face Transformers for integrating language models into RAG workflows.
• Cloud and Distributed Systems: Familiarity with cloud platforms (AWS, GCP, or Azure) for deploying and scaling multi-agent systems.
• Experimentation Tools: Experience with tools like Weights & Biases or TensorBoard for tracking RL experiments.
• Graph-Based Approaches: Knowledge of graph-based algorithms and tools (e.g., NetworkX) for agent relationships or task modeling.
• Knowledge Representation: Familiarity with knowledge graphs and ontologies for agent reasoning.
• Asynchronous Programming: Experience with multi-threading or asynchronous programming in Python for agent orchestration.

Tools and Technologies

• Languages and Libraries: Python, NumPy, pandas, PyTorch, TensorFlow, Stable-Baselines3.
• Multi-Agent and RL Frameworks: OpenAI Gym, PettingZoo, LangGraph, LangChain.AutoGen or CrewAI
• RAG and Vector Search Tools: Pinecone, FAISS, Weaviate, Milvus.
• Cloud Deployment: AWS, GCP, or Azure.
• Orchestration Tools: LangGraph, Docker, Kubernetes.
• Experiment Tracking: Weights & Biases, TensorBoard.

What We Offer

• Competitive salary, benefits and bonuses
• Opportunity to work on cutting-edge AI and multi-agent systems.
• Mentorship and training to accelerate your career growth.
• A collaborative and inclusive work environment with flexible hours.
• The chance to make an impact with innovative AI solutions.

How to Apply

Submit your resume, a cover letter, and any relevant project portfolio to HR Manager. Ms. Mashael Alhazmi at ].

We’re looking for passionate individuals excited to make a difference in the field of AI and multi-agent systems!