AI/ML Solution Architect

About the Role

Key Responsibilities

• Design end-to-end AI/ML solution architectures aligned with business objectives, scalability, security, and performance requirements.
• Architect solutions leveraging Generative AI, LLMs, NLP, Computer Vision, Machine Learning, Deep Learning, and AI Agents.
• Define architecture for LLM-based applications, RAG pipelines, AI agents, vector databases, embeddings, model fine-tuning, and inference systems.
• Evaluate and select appropriate AI/ML models, frameworks, platforms, tools, and cloud services.
• Design and implement MLOps/LLMOps strategies covering model development, deployment, monitoring, versioning, evaluation, and governance.
• Architect AI solutions across major cloud platforms such as AWS, Azure, and/or Google Cloud.
• Develop scalable data architectures and pipelines required for AI/ML workloads.
• Establish best practices around AI security, data privacy, responsible AI, model governance, and compliance.
• Conduct technical discovery sessions and translate complex business requirements into practical AI/ML solutions.
• Create solution architecture documents, technical proposals, HLDs, LLDs, architecture diagrams, POCs, and technology roadmaps.
• Collaborate with engineering teams to ensure successful implementation of the proposed architecture.
• Provide technical leadership and mentorship to architects, developers, data scientists, and ML engineers.
• Participate in client presentations, technical discussions, workshops, solution demonstrations, and pre-sales activities.
• Stay current with emerging AI technologies and assess their applicability to enterprise use cases.
• Drive architectural decisions, technology evaluations, and proof-of-concepts for emerging AI capabilities.

Required Technical Skills

• 12+ years of experience in software engineering, technology architecture, or solution architecture.
• Strong hands-on and architectural experience in AI/ML technologies.
• Excellent understanding of Generative AI and Large Language Models (LLMs).
• Experience with frameworks and technologies such as OpenAI, Azure OpenAI, LangChain, LangGraph, LlamaIndex, Hugging Face, or equivalent.
• Strong knowledge of RAG, embeddings, vector databases, prompt engineering, model evaluation, fine-tuning, and AI agents.
• Experience with Python and modern AI/ML frameworks.
• Strong understanding of Machine Learning, Deep Learning, NLP, Computer Vision, and predictive analytics.
• Experience designing production-grade ML/AI systems.
• Strong understanding of MLOps/LLMOps, CI/CD, model lifecycle management, monitoring, and observability.
• Experience with cloud platforms such as AWS, Microsoft Azure, and/or Google Cloud Platform.
• Knowledge of databases, data platforms, APIs, microservices, event-driven architectures, and distributed systems.
• Experience with Docker, Kubernetes, APIs, Git, CI/CD, and modern DevOps practices.
• Strong understanding of AI/ML security, data governance, privacy, and responsible AI principles.

Solution Architecture & Client Responsibilities

• Lead technical discussions with enterprise clients and C-level/technology stakeholders.
• Understand business problems and identify opportunities where AI/ML can deliver measurable value.
• Prepare AI/ML solution proposals, architecture blueprints, technical estimations, and implementation roadmaps.
• Support the sales and pre-sales team in technical discovery and proposal development.
• Present complex AI/ML concepts clearly to both technical and non-technical audiences.
• Conduct architecture reviews and provide recommendations on technology selection and solution design.
• Identify technical risks, dependencies, scalability concerns, and mitigation strategies.

Preferred Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
• Experience working with enterprise clients or digital transformation programs.
• Experience leading large-scale AI/ML transformation initiatives.
• Experience with Agentic AI and multi-agent architectures.
• Experience with AI governance, responsible AI, and enterprise AI adoption.
• Relevant cloud/AI certifications such as AWS, Azure, Google Cloud, or AI/ML certifications are an advantage.