Software Engineer

Role: SDE (LangGraph)

Experience Level: 5-7 years

Location: Remote

About the Role:

We are seeking an experienced Developer to join our team, focusing on enhancing agent performance evaluation, self-learning frameworks, and overall agent intelligence. This role involves working with advanced methodologies such as memory architectures, LLM fine-tuning, and optimization strategies to build highly capable, adaptive, and autonomous agents. The ideal candidate has 5+ years of Python experience, deep expertise in evaluating and improving agent performance, and strong proficiency with VLLM, Docker, and Kubernetes.

Key Responsibilities:

Agent Performance Evaluation

• Design and implement robust frameworks to evaluate agent performance across dimensions such as decision-making, learning speed, and adaptability.

Intelligence Enhancement

• Develop methodologies to improve agent intelligence, enabling better reasoning, adaptability, and autonomous problem-solving.

Memory Architectures

• Create advanced short-term and long-term memory architectures that help agents retain and apply past experiences to future decisions.

LLM Fine-Tuning

• Fine-tune and optimize Large Language Models (LLMs) to improve contextual understanding, reasoning, and task performance within agents.

Agent Evaluation Metrics

• Establish quantitative and qualitative metrics to assess agent intelligence, efficiency, and long-term adaptability.

Feedback Loops & Reinforcement Learning

• Implement reinforcement learning strategies and feedback loops that enable agents to autonomously optimize their performance.

Optimization for Fast Learning

• Use technologies such as VLLM to ensure fast and accurate learning, training, and inference within agent systems.

Containerized Deployment

• Utilize Docker and Kubernetes to deploy, scale, and orchestrate agent systems in production environments.

Basic Qualifications:

• 5+ years of Python development experience, with a strong focus on AI, machine learning, and agent-based systems.
• Proven expertise in agent performance evaluation and intelligence enhancement techniques.
• Strong experience with VLLM for fine-tuning and optimizing LLMs.
• Experience developing short-term and long-term memory architectures for intelligent agents.
• Deep knowledge of reinforcement learning, self-learning frameworks, and feedback-driven optimization.
• Proficiency with Docker and Kubernetes for containerization and orchestration.
• Expertise in building frameworks to assess and improve agent learning speed, decision accuracy, and adaptability.
• Hands-on experience with LLM fine-tuning and model optimization to improve reasoning and contextual understanding.

Preferred Qualifications:

• Familiarity with advanced memory networks, neural-symbolic integration, and other intelligence-enhancing techniques.
• Experience deploying, scaling, and managing intelligent agent systems in cloud-native environments.
• Background in developing custom evaluation frameworks and performance metrics for intelligent agents.