Gen AI Technical Lead/Architect

As an AI Architect specializing in Agentic AI and Generative AI, your primary responsibility will be to design, develop, and deploy cutting-edge autonomous AI systems. You will focus on building LLM-powered agents with memory, tool usage, planning, and reasoning capabilities to create intelligent, goal-oriented systems. Your role as a technical leader will involve overseeing AI initiatives from research and architecture design to deployment in cloud environments. Key Responsibilities: - Architect and construct LLM-based agents capable of autonomous task execution, memory management, tool usage, and multi-step reasoning. - Develop modular, goal-oriented agentic systems utilizing tools like LangChain, Auto-GPT, CrewAI, SuperAGI, and OpenAI Function Calling. - Design collaborative multi-agent ecosystems with negotiation, collaboration, and task delegation capabilities. - Integrate long-term and short-term memory (e.g., vector databases, episodic memory) into agents. - Develop, fine-tune, and optimize foundation models (LLMs, diffusion models) using TensorFlow, PyTorch, or JAX. - Apply model compression, quantization, pruning, and distillation techniques for efficient deployment. - Utilize cloud AI services such as AWS SageMaker, Azure ML, Google Vertex AI for scalable model training and serving. - Lead research in Agentic AI, LLM orchestration, and advanced planning strategies (e.g., ReAct, Tree of Thought, Reflexion, Autoformalism). - Stay up-to-date with state-of-the-art 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. - Exhibit strong coding skills 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. Qualifications Required: - 8-14 years of experience in AI/ML, with at least 2+ years of hands-on experience with Agentic AI systems. - Proven track record in 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 experience 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.,