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)