
Artificial Intelligence Visionary
2 days ago
Job Title: Artificial Intelligence Technical Lead/Architect
About the Position
We are seeking an exceptional AI architect with deep expertise in Agentic AI and Generative AI to lead the design, development, and deployment of next-generation autonomous AI systems. This role is pivotal in building Large Language Model (LLM) powered agents, integrating memory, tool use, planning, and reasoning capabilities to create intelligent, goal-driven systems.
As a technical leader, you will drive end-to-end AI initiatives—from cutting-edge research and architecture design to scalable deployment in cloud environments.
Main Responsibilities
- Design and build LLM-based agents capable of autonomous task execution, memory management, tool usage, and multi-step reasoning.
- Develop modular, goal-oriented agentic systems using tools like LangChain, Auto-GPT, CrewAI, SuperAGI, and OpenAI Function Calling.
- Create multi-agent ecosystems with collaboration, negotiation, and task delegation capabilities.
- Integrate long-term and short-term memory (e.g., vector databases, episodic memory) into agents.
Generative AI & Model Optimization
- Develop, fine-tune, and optimize foundation models (LLMs, diffusion models) using TensorFlow, PyTorch, or JAX.
- Apply model compression, quantization, pruning, and distillation techniques for deployment efficiency.
- Leverage cloud AI services such as AWS SageMaker, Azure ML, Google Vertex AI for scalable model training and serving.
AI Research & Innovation
- Lead research in Agentic AI, LLM orchestration, and advanced planning strategies (e.g., ReAct, Tree of Thought, Reflexion, Autoformalism).
- Stay current with State-of-the-Art (SOTA) research; contribute to whitepapers, blogs, or top-tier conferences (e.g., NeurIPS, ICML, ICLR).
- Evaluate new architectures like BDI models, cognitive architectures (e.g., ACT-R, Soar), or neuro-symbolic approaches.
Programming & Systems Engineering
- Strong coding proficiency in Python, CUDA, and TensorRT for model acceleration.
- Experience with distributed computing frameworks (e.g., Ray, Dask, Apache Spark) for training large-scale models.
- Design and implement robust MLOps pipelines with Docker, Kubernetes, MLflow, and CI/CD systems.
Required Skills & Qualifications
- 8–14 years of experience in AI/ML, with at least 2+ years hands-on with Agentic AI systems.
- Proven experience building, scaling, and deploying agent-based architectures.
- Strong theoretical foundation in machine learning, deep learning, NLP, and reinforcement learning.
- Familiarity with cognitive architectures, decision-making, and planning systems.
- Hands-on with LLM integration and fine-tuning (e.g., OpenAI GPT-4, Claude, LLaMA, Mistral, Gemini).
- Deep understanding of prompt engineering, function/tool calling, retrieval-augmented generation (RAG), and memory management in agentic systems.
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