AI Solutions Architect

Role & responsibilities

• Design and architect scalable, secure, and production-ready AI/ML and Generative AI solutions aligned with business requirements.
• Translate complex business problems into AI-driven solution architectures, technical roadmaps, and implementation strategies.
• Lead the architecture and development of LLM, RAG, AI agent, NLP, computer vision, and machine learning solutions.
• Evaluate and select appropriate AI models, cloud services, frameworks, databases, and technology stacks.
• Design end-to-end AI architectures across AWS, Azure, or GCP, including data, compute, APIs, security, and deployment layers.
• Define best practices for MLOps/LLMOps, model deployment, monitoring, evaluation, scalability, reliability, and cost optimization.
• Work closely with data scientists, ML engineers, software engineers, DevOps teams, product managers, and business stakeholders.
• Develop technical prototypes and proof-of-concepts to validate AI solutions and demonstrate business value.
• Ensure AI solutions meet enterprise standards for security, privacy, governance, compliance, and responsible AI.
• Provide technical leadership, architecture guidance, code/design reviews, and mentorship to engineering teams.
• Collaborate with clients and senior stakeholders to understand requirements, present solution options, and communicate technical concepts clearly.
• Stay current with emerging AI/GenAI technologies and identify opportunities to integrate them into enterprise solutions.

Preferred candidate profile

• 5 to 10 years of experience in software engineering, AI/ML, cloud architecture, solution architecture, or related technology roles.
• Strong hands-on experience with AI/ML and Generative AI, particularly LLMs, RAG, AI agents, prompt engineering, and model evaluation.
• Strong programming skills in Python and familiarity with modern AI/ML frameworks and libraries.
• Proven experience designing and implementing cloud-native solutions on AWS, Azure, or GCP.
• Strong understanding of system design, distributed systems, microservices, APIs, databases, data pipelines, and enterprise architecture.
• Experience with MLOps/LLMOps, CI/CD, Docker, Kubernetes, model deployment, monitoring, and observability.
• Familiarity with vector databases, embedding models, LangChain/LlamaIndex, AI APIs, and open-source/commercial LLMs.
• Strong understanding of AI security, data privacy, governance, responsible AI, and enterprise integration patterns.
• Ability to convert business requirements into scalable technical architectures and clearly communicate solutions to technical and non-technical stakeholders.
• Demonstrated experience leading technical initiatives, PoCs, architecture decisions, and cross-functional engineering teams.
• Strong analytical, problem-solving, communication, consulting, and stakeholder-management skills.
• Relevant certifications in AWS/Azure/GCP, AI/ML, cloud architecture, or enterprise architecture are an advantage.