
AI Engineer for Automation and Reasoning
18 hours ago
About the Role
As an AI Engineer at Beam, you will play a crucial role in the development, implementation, and optimization of our AI models and systems. You will work closely with other engineers and product teams to build intelligent systems that improve business processes through automation and data-driven insights.
This is a mid-senior level position that requires strong programming skills in Python, along with experience in SQL and familiarity with other programming languages. The ideal candidate should have experience working with LangChain, LlamaIndex, or other frameworks for building AI Agents and LLM-powered applications.
The responsibilities of this role include researching, designing, and building AI-powered features that enhance our AI Agents' capabilities. This involves investigating emerging AI techniques, including reasoning, planning, and memory architectures, to enhance AI Agents' autonomy and adaptability.
- Key Responsibilities:
- Research & Innovation: Investigate emerging AI techniques to enhance AI Agents' autonomy and adaptability.
- Feature Development: Design and implement new agent capabilities.
- LLM Optimization: Leverage prompt engineering, fine-tuning, and retrieval-augmented generation (RAG) techniques to improve the performance and reliability of large language models (LLMs).
- Agent Orchestration: Develop systems that allow AI Agents to break down complex tasks, collaborate with other agents, and request human input when needed.
- Data & Workflow Integration: Work closely with data engineers and product teams to build robust pipelines for AI Agents to access structured and unstructured data.
- Performance & Scalability: Optimize AI systems for efficiency, ensuring that agents can handle real-world constraints such as cost, latency, and interpretability.
- Continuous Learning & Improvement: Stay updated with the latest advancements in AI, machine learning, and software engineering, incorporating cutting-edge research into practical applications.
- Code Quality & Reliability: Participate in code reviews, testing, and deployment pipelines to ensure high-quality and maintainable AI features.
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