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Agentic AI Engineer
4 weeks ago
Responsible for Lead the design and development of an Agentic AI platform. Deep expertise in machine learning, system architecture, and AI agent frameworks to build scalable, autonomous systems. Architect and implement core systems for agent-based AI workflows. Design and deploy LLM-based pipelines, agent orchestration, and vector-based memory systems. Develop and optimize ML models, pipelines, and orchestration logic. Drive technical strategy, tooling, and infrastructure decisions. Architect and implement agentic AI systems leveraging GCP services (Vertex AI, BigQuery, Cloud Functions, Pub/Sub, etc.). Requirements: Several years of industry experience in AI/ML and data engineering, with a track record of working in large-scale programs and solving complex use cases using GCP AI Platform/Vertex AI. Agentic AI Architecture: Exceptional command in Agentic AI architecture, development, testing, and research of both Neural-based & Symbolic agents, using current-generation deployments and next-generation patterns/research. Agentic Systems: Expertise in building agentic systems using techniques including Multi-agent systems, Reinforcement learning, flexible/dynamic workflows, caching/memory management, and concurrent orchestration. Proficiency in one or more Agentic AI frameworks such as LangGraph, Crew AI, Semantic Kernel, etc. Python Proficiency: Expertise in Python language to build large, scalable applications, conduct performance analysis, and tuning. Prompt Engineering: Strong skills in prompt engineering and its techniques including design, development, and refinement of prompts (zero-shot, few-shot, and chain-of-thought approaches) to maximize accuracy and leverage optimization tools. IR/RAG Systems: Experience in designing, building, and implementing IR/RAG systems with Vector DB and Knowledge Graph. Model Evaluation: Strong skills in the evaluation of models and their tools. Experience in conducting rigorous A/B testing and performance benchmarking of prompt/LLM variations, using both quantitative metrics and qualitative feedback. Technical Skills Required: Programming Languages: Proficiency in Python is essential. Agentic AI : Expertise in LangChain/LangGraph, CrewAI, Semantic Kernel/Autogen and Open AI Agentic SDK Machine Learning Frameworks: Experience with TensorFlow, PyTorch, Scikit-learn, and AutoML.Generative AI: Hands-on experience with generative AI models, RAG (Retrieval-Augmented Generation) architecture, and Natural Language Processing (NLP). Cloud Platforms: Familiarity with Google Cloud Platform (GCP). Data Engineering: Proficiency in data preprocessing and feature engineering. Version Control: Experience with GitHub for version control. Data Science Practices: Skills in building models, testing/validation, and deployment. · Collaboration: Experience working in an Agile framework. RAG Architecture: Experience with data ingestion, data retrieval, and data generation using optimal methods such as hybrid search. Google Cloud Platform:
