About the role
In this role, you will work at the intersection of artificial intelligence, software engineering, and cognitive architectures to build AI agents that can reason, plan, learn, and act with high levels of autonomy across dynamic environments. The ideal candidate brings deep experience in building agent-based systems, reinforcement learning, or multi-agent frameworks, along with a passion for pushing the boundaries of AI that can operate with initiative and self-direction.
Roles and Responsibilities:
- Develop agentic AI systems capable of autonomous decision-making, task decomposition, and adaptive behavior in real-world or simulated environments.
- Design and implement cognitive architectures, memory systems, and planning algorithms to enable goal-driven, self-reflective agents.
- Integrate large language models (LLMs), symbolic reasoning, and other AI components into cohesive agent frameworks.
- Collaborate with cross-functional teams (e.g., product, UX, cloud engineering) to deploy agents in production environments.
- Optimize performance of agentic systems for scalability, latency, and robustness.
- Conduct experiments and evaluations on agent behavior, adaptability, and long-term autonomy.
- Stay current with research and best practices in autonomous agents, AI planning, reinforcement learning, and related domains.
Required Skills & Qualifications:
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Robotics, or a related field
- Experience in AI/ML software development, with a focus on autonomous agents or AI planning.
- Strong proficiency in Python and familiarity with frameworks such as Lang Chain, Auto Gen, or custom agentic tool kits.
- Experience with LLMs, transformers, and integrating language models into agent workflows.
- Solid understanding of one or more of: reinforcement learning, cognitive architectures, symbolic reasoning, or decision-making systems.
- Experience with cloud-based deployment of AI systems (e.g., AWS, GCP, Azure)
- Published work in agentic AI, multi-agent systems, or autonomous learning.
- Contributions to open-source agentic AI tools or frameworks.