AI Technical Lead/ Architect Damco Solutions

8.0 - 14.0 years

0 Lacs

noida, uttar pradesh

On-site

You are an exceptional AI Architect with expertise in Agentic AI and Generative AI, responsible for leading the design, development, and deployment of next-generation autonomous AI systems. Your role involves building LLM-powered agents with memory, tool use, planning, and reasoning capabilities to create intelligent, goal-driven systems. As a technical leader, you will oversee end-to-end AI initiatives, from research and architecture design to deployment in cloud environments. Your key responsibilities include designing and implementing LLM-based agents for autonomous task execution, memory management, tool usage, and multi-step reasoning. You will develop modular, goal-oriented agentic systems using tools like LangChain, Auto-GPT, CrewAI, SuperAGI, and OpenAI Function Calling. Additionally, you will design multi-agent ecosystems with collaboration, negotiation, and task delegation capabilities while integrating long-term and short-term memory into agents. You will also be responsible for developing, fine-tuning, and optimizing foundation models like LLMs and diffusion models using TensorFlow, PyTorch, or JAX. Applying model compression, quantization, pruning, and distillation techniques for deployment efficiency will be part of your role. Leveraging cloud AI services such as AWS SageMaker, Azure ML, and Google Vertex AI for scalable model training and serving is also crucial. Your tasks will include leading research in Agentic AI, LLM orchestration, and advanced planning strategies. Staying updated with state-of-the-art research and contributing to whitepapers, blogs, or conferences like NeurIPS, ICML, and ICLR will be expected. Evaluating new architectures such as BDI models, cognitive architectures, or neuro-symbolic approaches will be part of your responsibilities. Strong coding proficiency in Python, CUDA, and TensorRT for model acceleration is required, along with experience in distributed computing frameworks like Ray, Dask, and Apache Spark for training large-scale models. Designing and implementing robust MLOps pipelines using Docker, Kubernetes, MLflow, and CI/CD systems is essential. To excel in this role, you should have at least 8-14 years of experience in AI/ML, with a minimum of 2+ years of hands-on experience with Agentic AI systems. Proven experience in building, scaling, and deploying agent-based architectures is necessary. A strong theoretical foundation in machine learning, deep learning, NLP, and reinforcement learning is crucial, along with familiarity in cognitive architectures, decision-making, and planning systems. Hands-on experience with LLM integration and fine-tuning, including OpenAI GPT-4, Claude, LLaMA, Mistral, and Gemini, is required. Preferred qualifications include publications or open-source contributions in Agentic AI or Generative AI, experience with simulation environments like OpenAI Gym or Unity ML-Agents for training/test agents, and knowledge of safety, ethics, and alignment in autonomous AI systems.,

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