AI/ML Developer – Team Leader

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    Openings : 01 Experience : 6+ years

    Location : Work From Home


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      Complimentary Health Insurance

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    Job Description:

    We are looking for a skilled AI/ML Developer with 6+ 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.


    Job Responsibility:

    • 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 & Qualification:

    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.

    Nice-to-Have Skills:

    • Experience with Graph Neural Networks (GNNs) and Neo4j for AI reasoning.
    • Familiarity with AutoGPT, BabyAGI, or other autonomous agent frameworks.
    • Experience in agent-based modeling for financial, gaming, or industrial automation applications.

    Interview Process:

    1. HR Round
    2. Technical Round
    3. Practical Round
    4. Salary Negotiation
    5. Offer Release

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