5 Artoon Solutions Jobs

Job Description

Job Overview:

We are looking for a skilled AI/ML Developer with 3+ years of experience in designing and
developing AI agents. The ideal candidate should have expertise in reinforcement learning
(RL), LLM fine-tuning, multi-agent systems, and real-time decision-making models. You
will work on developing intelligent AI agents that interact autonomously, learn from their
environment, and optimize performance across various applications.

Key Responsibilities:
● AI Agent Development: Design, build, and deploy autonomous AI agents for different
applications such as chatbots, automation bots, trading bots, game AI, and
real-world decision-making agents.
● Reinforcement Learning (RL): Develop RL-based agents using Deep Q Networks
(DQN), PPO, A3C, SAC, and other RL algorithms.
● LLM & Conversational AI: Fine-tune and integrate LLMs (ChatGPT, LLaMA, Falcon,
Gemini, Claude) into AI agents for contextual understanding and advanced interactions.
● Multi-Agent Systems: Implement and optimize multi-agent environments where AI
agents collaborate or compete to achieve tasks.
● Memory & Planning in AI Agents: Work on vector databases (Pinecone, Weaviate,
ChromaDB) and retrieval-augmented generation (RAG) to improve AI agent memory
and planning capabilities.
● Real-Time Decision Making: Develop AI models that make real-time decisions based
on reinforcement learning, probabilistic models, or imitation learning.
● Simulation & Training: Use environments like OpenAI Gym, Unity ML-Agents,
Mujoco, or custom simulations to train AI agents.
● Deployment & Integration: Deploy AI agents into real-world applications, games,
customer service platforms, automation workflows, and web interfaces using APIs.

Required Skills:

✅ Programming Languages: Strong expertise in Python (PyTorch, TensorFlow, JAX).
✅ AI Agent Frameworks: Experience with LangChain, AutoGen, OpenAI APIs, Hugging
Face Transformers, CrewAI.
✅ Reinforcement Learning: Knowledge of Deep Q-Learning, Actor-Critic Methods (PPO,
A3C, SAC), Monte Carlo Tree Search (MCTS).
✅ LLM Integration: Experience in LLM fine-tuning, prompt engineering, RAG, LangChain,
OpenAI APIs.
✅ Multi-Agent Systems: Experience in developing and training multiple interacting
agents in real-time applications.
✅ Memory & Knowledge Retrieval: Knowledge of vector databases (Pinecone, Weaviate,
ChromaDB, FAISS) for long-term AI memory.
✅ Simulation & Game AI: Experience with OpenAI Gym, Mujoco, Unity ML-Agents, or
other simulation tools.
✅ Model Deployment: Experience deploying AI agents via Flask, FastAPI, Docker,
Kubernetes, cloud platforms (AWS, Azure, GCP).
✅ Version Control & DevOps: Proficiency with Git, CI/CD pipelines, and containerized ML
workflows.