AI Systems Architect

We are seeking a highly skilled and forward-thinking expert in designing, deploying, and operationalizing modern AI solutions using Generative AI, Agentic AI frameworks, Retrieval-Augmented Generation (RAG), and Model Context Protocol (MCP) for intelligent multi-agent orchestration.

This role requires building context-aware AI agents capable of dynamic memory management, tool-based reasoning, multi-modal interaction, and consistent state maintenance across distributed AI ecosystems. The successful candidate will work at the forefront of autonomous AI system design integrating multi-model workflows, RAG pipelines, tool-augmented agents, and persistent context protocols.

• Design, develop, and deploy advanced AI systems leveraging LLMs, multi-modal models, and autonomous agent frameworks.
• Architect and implement Retrieval-Augmented Generation (RAG) pipelines integrated with vector databases for dynamic, enterprise-safe context retrieval.
• Develop and manage context-aware AI agents leveraging Model Context Protocol (MCP) or equivalent structured context management mechanisms for persistent memory, multi-agent collaboration, and session continuity.
• Implement context propagation, state serialization, and contextual handoff mechanisms across distributed agents, tools, and reasoning chains.
• Integrate AI systems with cloud APIs, vector databases, plugin architectures, and orchestration frameworks for multi-step task execution.
• Optimize memory management, prompt engineering, and dynamic context windows for complex, long-running AI workflows.
• Lead AI agent orchestration initiatives using frameworks like LangChain, Semantic Kernel, CrewAI, or AutoGen, with MCP integration for context tracing and governance.
• Stay ahead of emerging trends in multi-agent LLM ecosystems, autonomous reasoning frameworks, and context-aware AI protocol standards like MCP.

• 3+ years of AI engineering experience, with direct expertise in:
• Generative AI (GenAI) frameworks (OpenAI, Hugging Face, Anthropic, Mistral)
• Agentic AI orchestration frameworks (LangChain, Semantic Kernel, AutoGen, CrewAI)
• Retrieval-Augmented Generation (RAG) system design and deployment
• Model Context Protocol (MCP) or structured context management solutions in multi-agent, multi-tool AI workflows
• Strong software engineering skills in Python, including AI/ML libraries like PyTorch, TensorFlow, Transformers, and RAG toolkits.
• Hands-on experience working with vector databases (Pinecone, FAISS, Weaviate, Chroma) and plugin-based AI architectures.
• Strong understanding of AI memory management, session context propagation, and multi-agent communication protocols.
• Programming framework/language: Python, JS or Java.
• Vector databases (Pinecone, FAISS, Weaviate, Chroma) and plugin-based AI architectures.
• Experience with ML frameworks like TensorFlow, PyTorch, or Scikit-learn.
• Solid understanding of statistics, data structures, and algorithms.

• Experience integrating multi-modal models (text, image, audio, video) into agentic workflows.
• Familiarity with AI safety, observability, and context integrity management in distributed AI systems.
• Exposure to LLMOps pipelines, prompt optimization techniques, and AI agent performance tuning.
• Experience contributing to open-source AI orchestration frameworks or context management libraries.
• Understanding of AI alignment, ethical AI principles, and responsible AI design.
• Experience with Deep learning in NLP, computer vision, or reinforcement learning.

• Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field.
• 4+ years of experience working as a software engineer developing commercial software.
• Experience using collaborative tools such as: Slack, Microsoft Teams, and Jira.