AI Solution Architect

We are seeking an experienced AI Solution Architect to be responsible for designing and implementing robust, scalable, and reliable AI solutions for TOP 500 Fortune clients. This includes generative AI systems, autonomous AI agents, and operator-based solutions that leverage cutting-edge techniques and frameworks (e.g., LangChain, Flowise, RAGflow). The role requires expertise in data architecture, machine learning, cloud services, and software engineering — alongside deep knowledge of how to ensure accurate, trustworthy, and high-quality AI outputs. Responsibilities Plan, architect, and deploy AI systems, including Generative AI models and AI agents Integrate AI solutions into existing business and data infrastructures Evaluate and adopt suitable RAG (Retrieval-Augmented Generation) frameworks such as LangChain, Flowise, and RAGflow Ensure that frameworks and technologies align with enterprise goals and compliance requirements Design robust data pipelines for ingestion, transformation, and indexing Implement best practices for vector databases, semantic search, and knowledge-base indexing Devise prompt-engineering and response-validation mechanisms for reliable AI outputs Incorporate thorough testing, quality control, and RLHF (Reinforcement Learning from Human Feedback) techniques where applicable Architect agent-based solutions that can autonomously interact with systems and make informed decisions Integrate operator-based flows that augment human workflows Ensure solutions are scalable, secure, and manageable (MLOps/DevOps best practices) Uphold responsible AI principles — transparency, fairness, privacy, and ethical data usage Effectively communicate AI strategies and solution designs to both technical and non-technical stakeholders Collaborate across multidisciplinary teams to ensure AI initiatives are aligned with organizational objectives Requirements 8+ years of experience in software development and solution architecture, with a proven record of delivering enterprise solutions Deep understanding of Generative AI and large language models (LLMs) Practical experience in prompt engineering and fine-tuning techniques Hands-on experience with RAG frameworks: LangChain, Flowise, RAGflow Expertise in deploying agentic and operator-based solutions using autonomous AI agents Familiarity with indexing and semantic search technologies (e.g., Pinecone) Expertise in building robust knowledge-base indexes for accurate information retrieval Skills in creating and managing AI agents for automation, cognitive architectures, and autonomous operations Understanding reinforcement learning and cognitive agent architectures Knowledge of AI ethics, fairness, transparency, accountability, and responsible AI practices Implementation of governance frameworks and ethical guidelines Integrating AI seamlessly into existing operational workflows Familiarity with FastAPI, Kubernetes, and DevSecOps/MLOps practices Knowledge of semantic search, cognitive architectures, and reinforcement learning Continuous learning mindset and proactive exploration of emerging AI technologies Excellent stakeholder management and effective communication skills Capability to translate complex AI concepts into actionable insights for technical and non-technical audiences Excellent written and verbal communication skills in English (B2+ level)