该职位来源于猎聘 Key Responsibilities:
- Architect and implement a modular, microservices-based agentic AI platform enabling multi-agent orchestration and autonomous workflows.
- Develop and integrate autonomous agents leveraging LLMs, retrieval-augmented generation (RAG), reinforcement learning, and other deep learning models for task planning, execution, and decision-making.
- Fine-tune and benchmark LLMs, graph neural networks, and domain-specific deep learning architectures to optimize performance for specialized tasks.
- Build efficient inference pipelines, including caching, batching, and real-time interactivity optimizations.
- Design and develop APIs and SDKs for seamless integration with external tools, data sources, and AI assistants (e.g., literature databases, simulation engines).
- Implement knowledge mapping, semantic search, dynamic knowledge graphs, and agent memory systems to enhance agent reasoning and knowledge retrieval.
- Incorporate guardrails such as human-in-the-loop, response verification, reflection, and hallucination mitigation to improve agent reliability and accuracy.
- Collaborate with cross-functional Agile teams including product managers, UX designers, and engineers to deliver impactful AI-driven solutions.
- Develop evaluation frameworks and testing systems for continuous agent performance monitoring and improvement.
- Document system designs, APIs, operational runbooks, and best practices.
Required Qualifications
- Advanced degree (Master's or Ph.D.) in Computer Science, Artificial Intelligence, Machine Learning, or related fields, or equivalent experience. 2.5+ years of experience in machine learning, deep learning, NLP, and autonomous agent development.
- Proven expertise in fine-tuning large language models (e.g., GPT, Claude) and other deep learning architectures for domain-specific applications.
- Strong programming skills in Python and familiarity with ML frameworks such as
TensorFlow or PyTorch.
- Experience designing and deploying scalable microservices and distributed systems on cloud platforms (AWS, Azure, GCP).
- Proficiency with agentic AI frameworks and tools such as LangChain, LangGraph, AutoGen, or similar.
- Experience with vector databases, semantic search, retrieval-augmented generation (RAG), and knowledge graph construction.
- Familiarity with reinforcement learning and agent-based modeling techniques.
- Hands-on experience with containerization (Docker, Kubernetes) and CI/CD pipelines.
- Strong problem-solving skills, analytical thinking, and ability to work in fast-paced, collaborative environments. Preferred Skills:
- Experience with AI safety, hallucination prevention, and human-in-the-loop methodologies.
- Background in computational research pipelines, simulation integration, or scientific workflows.
- Contributions to AI research, publications, or open-source projects in agentic AI or related fields.
- Knowledge of message queuing systems (Kafka, AWS SQS) and event-driven architectures.
- Familiarity with AI-centered coding workflows using tools like Copilot, Cursor AI, or similar.