Solution Architect
Role details
Job location
Tech stack
Job description
We are seeking an experienced, highly adaptable, curious, and fast-thinking AI Solutions Architect to design, prototype, and deliver innovative AI capabilities across internal use cases. The ideal candidate combines strong foundational understanding of AI/ML technologies with a proactive drive to stay ahead of industry advancements, especially in generative AI and emerging architectures. This role bridges business needs and technical execution, architecting dynamic solutions that leverage LLMs, traditional ML, data pipelines, RAG, agents, and enterprise integrations. CORE RESPONSIBILITIES: Solution Architecture & Innovation
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Translate business challenges into well-scoped AI solutions, balancing feasibility, value, cost, and speed.
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Architect end-to-end AI systems, including data ingestion, model training, inference pipelines, monitoring, and governance.
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Design and refine LLM/RAG architectures, agent workflows, and prompt engineering patterns
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Rapidly explore emerging tools/techniques to extend AI capabilities across the organization
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Build reusable reference architectures and best practices for internal teams Technical Leadership & Execution
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Partner with engineering, data science, and product teams to guide implementation
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Conduct PoCs, prototypes, and pilots to validate technical suitability before scaling
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Ensure solutions meet performance, security, compliance, and cost-efficiency requirements
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Integrate AI capabilities into existing systems, both cloud and legacy
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Work with MLOps/DevOps to establish robust CI/CD, observability, and lifecycle management Strategy, Governance & Cross-Functional Collaboration
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Complement the Product team by defining the technical AI/ML roadmap, assessing feasibility, shaping the use-case pipeline, and specifying the architecture required to deliver prioritized initiatives
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Provide expertise on responsible AI, privacy, and risk-aware design
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Communicate complex concepts to stakeholders at all levels
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Mentor engineers and data scientists on architecture, quality, and emerging AI capabilities
Requirements
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Bachelor's or Master's degree in Computer Science, Engineering, or related field. OR equivalent work experience.
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Additional certifications in AI/ML technologies are preferred Technical Background
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7+ years in solution architecture with proficiency in data architecture, including data pipelines, warehousing / Lakehouse concepts, APIs and integration patterns
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Strong understanding of security, privacy, compliance, and responsible AI principles, including access control, data protection, and risk mitigation
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Deep understanding of machine learning, generative AI, LLMs, RAG, prompt engineering, vector databases, and model evaluation frameworks
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Experience translating business requirements into solution architectures, technical roadmaps, and implementation plans
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Experience working cross-functionally with engineering, product, data teams, and business stakeholders to deliver measurable outcomes
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Knowledge of MLOps/LLMOps practices such as CI/CD, model monitoring, observability, versioning, governance, and lifecycle management Mindset & Soft Skills
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Exceptionally curious, adaptive, and proactive, stays ahead of fast-changing AI technologies
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Fast learner with ability to shift between conceptual and hands-on tasks
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Strong problem solver with a "builder" mentality
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Comfortable with ambiguity, rapid experimentation, and iterative design
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Excellent communicator to both technical and business audiences
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Collaborative and supportive partner to cross-functional teams