AI Solution Architect

About the Role We are seeking an experienced AI Solution Architect to lead the design and delivery of enterprise-grade AI solutions across Generative AI, Agentic AI, and Computer Vision domains. This role combines strategic thinking with hands-on technical leadership, translating complex business challenges into scalable, secure, and production-ready AI architectures. You will collaborate with data scientists, ML engineers, product teams, and cloud architects to define technical direction, establish AI best practices, and drive the successful implementation of innovative AI solutions that deliver measurable business value. Key Responsibilities • Design and govern end-to-end AI architectures for Generative AI, Agentic AI, and Computer Vision solutions. • Architect multi-agent systems, RAG frameworks, LLM integrations, and knowledge-based AI platforms. • Define reusable AI design patterns, reference architectures, and deployment standards. • Lead architecture decisions on model selection, orchestration frameworks, vector databases, and AI infrastructure. • Establish LLMOps and MLOps best practices for deployment, monitoring, scalability, and governance. • Architect computer vision solutions covering detection, classification, OCR, tracking, and multimodal AI use cases. • Collaborate with business stakeholders to assess AI opportunities and define technology roadmaps. • Ensure AI solutions comply with responsible AI, security, privacy, and regulatory requirements. • Mentor technical teams and lead architecture reviews across AI projects. Required Qualifications • Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field. • 7+ years of experience in software engineering, machine learning, or AI solution development. • Minimum 3 years of experience in architecture or technical leadership roles. • Hands-on experience in at least two of the following areas: • Generative AI / LLMs • Agentic AI / Multi-Agent Systems • Computer Vision • Strong experience with frameworks such as LangChain, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or similar technologies. • Expertise in cloud platforms (Azure, AWS, or Google Cloud), containerization, and Kubernetes. • Experience with vector databases, embeddings, RAG architectures, and scalable AI systems. • Proficiency in Python and modern AI/ML frameworks including PyTorch, TensorFlow, and OpenCV. • Excellent communication and stakeholder management skills. Preferred Qualifications • Experience building multimodal AI solutions combining text, image, and video data. • Knowledge of AI governance, model evaluation, red-teaming, and responsible AI frameworks. • Experience with real-time AI inference, edge AI, or video analytics solutions. • Background in regulated industries such as government, finance, healthcare, or manufacturing. • Open-source contributions or published AI-related research. Show more Show less

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field. 7+ years of experience in software engineering, machine learning, or AI solution development. Minimum 3 years of experience in architecture or technical leadership roles. Hands-on experience in at least two of the following areas: Generative AI / LLMs; Agentic AI / Multi-Agent Systems; Computer Vision. Strong experience with frameworks such as LangChain, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or similar technologies. Expertise in cloud platforms (Azure, AWS, or Google Cloud), containerization, and Kubernetes. Experience with vector databases, embeddings, RAG architectures, and scalable AI systems. Proficiency in Python and modern AI/ML frameworks including PyTorch, TensorFlow, and OpenCV. Excellent communication and stakeholder management skills.

Design and govern end-to-end AI architectures for Generative AI, Agentic AI, and Computer Vision solutions. Architect multi-agent systems, RAG frameworks, LLM integrations, and knowledge-based AI platforms. Define reusable AI design patterns, reference architectures, and deployment standards. Lead architecture decisions on model selection, orchestration frameworks, vector databases, and AI infrastructure. Establish LLMOps and MLOps best practices for deployment, monitoring, scalability, and governance. Architect computer vision solutions covering detection, classification, OCR, tracking, and multimodal AI use cases. Collaborate with business stakeholders to assess AI opportunities and define technology roadmaps. Ensure AI solutions comply with responsible AI, security, privacy, and regulatory requirements. Mentor technical teams and lead architecture reviews across AI projects.