AI Engineer

34ML

Responsibilities:

• Agentic Workflow Development: Design and build complex, stateful agentic workflows, leveraging the advanced capabilities of LangChain and LangGraph.
• Prompt & Chain Engineering: Develop and test prompt templates, retrieval chains (RAG), and agentic components to achieve high accuracy and performance.
• Production Integration: Implement APIs and integrate them into scalable applications.
• Monitoring & Observability: Participate in debugging, troubleshooting, and continuous monitoring of agentic workflows using specialized tools like LangSmith.
• Evaluation: Establish and execute evaluation frameworks for both predictive and generative systems, focusing on performance, accuracy, and security.
• Infrastructure Reliability: Contribute to troubleshooting complex AI infrastructure issues and ensuring the stability and production reliability of deployed systems.
• Documentation: Maintain clear and thorough documentation of data flows, model architectures, agent logic, and experiment results.

Skills:

• Experience: 1-3 years of hands-on professional experience in an AI, ML, Data Science, or Software Engineering role.
• Education: Bachelor's degree in Computer Science, or Engineering.
• Programming: Must be proficient in writing clean, efficient, and production-grade Python.
• Foundational CS: Solid knowledge of data structures, algorithms, and use of Git for collaborative software development.
• LLM Ecosystem: Familiarity with the Large Language Model ecosystem, including the OpenAI API and models available through Hugging Face.
• Agent Frameworks: Hands-on experience with or deep conceptual understanding of LangChain, LangGraph, and LangSmith.
• Networking: Knowledge of core Networking concepts required for API interaction and endpoint integration (e.g., REST, basic cloud networking).
• Continuous Learning: Committed to continuous learning and staying current with rapid advances in machine learning, generative AI, and agent frameworks (LangChain, LangGraph, etc.).