AI Solution Architect
Amazon Web Services (AWS) · Mumbai, Maharashtra, India
- On site
- Full-time
- $150,000 / year
- Mumbai, Maharashtra, India
Job highlights
- Architect AI solutions for diverse customer needs.
- Advise customers on GenAI/ML adoption strategies.
- Influence AWS AI service roadmap.
- Develop cloud-native GenAI/ML architectural patterns.
- Lead technical workshops and knowledge sharing.
About the role
AI Solution Architect at Amazon Web Services (AWS)
Amazon Web Services (AWS) is seeking a hands-on AI Specialist Solution Architect (SSA) to lead the next phase of AI adoption. As an SSA, you will be a technologist with deep domain-specific expertise, capable of addressing advanced concepts and feature designs. You will work within the AWS sales organization to solve complex customer challenges with scalable, flexible, and resilient technical architectures.
Key Job Responsibilities
- Build technical relationships with customers of all sizes, acting as their trusted advisor for adopting GenAI/ML and Agentic technologies.
- Manage the overall technical relationship with AWS customers, providing recommendations on security, cost, performance, reliability, and operational efficiency to accelerate GenAI/ML and Agentic projects.
- Serve as the voice of the customer internally, sharing needs and influencing the roadmap of AWS GenAI/ML and Agentic features.
- Translate technology into tangible solutions, defining cloud-native GenAI/ML and Agentic architectural patterns for various use cases.
- Participate in creating and sharing best practices, technical content, and new reference architectures (e.g., white papers, code samples, blog posts).
- Evangelize and educate about running GenAI/ML and Agentic workloads on AWS technology through workshops, user groups, public speaking, and online content.
- Lead hands-on deep dives and technical workshops, contributing reusable code, reference architectures, and internal technical assets for the broader engineering organization.
Basic Qualifications
- 7+ years of experience in design, implementation, operations, or consulting with distributed applications.
- 5+ years of experience managing technical, enterprise customer-facing resources or equivalent.
- Experience communicating complex concepts clearly and effectively to diverse audiences across different functions.
- Experience leading engineering discussions around technology decisions and strategy for a product.
- 5+ years of IT systems or relevant commercial production environment experience.
- Experience developing and deploying LLMs in production on GPUs, Neuron, TPU, or other AI acceleration hardware, or experience designing/architecting new and existing systems.
- Hands-on experience with AWS ecosystems (including Bedrock, AgentCore, SageMaker) for secure, private-network AI environments, and practical experience with Retrieval-Augmented Generation using embeddings, vector stores, and semantic search optimization.
Preferred Qualifications
- Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience with full software development life cycle.
- Experience engaging and influencing C-level executives, both business and technical.
- Cloud Technology Certification or AWS Professional level certification.
- Experience developing solutions and executing plans on complex projects.
- Experience leading and influencing your team or organization.
- Master's degree or above in computer science, mathematics, statistics, machine learning, or equivalent quantitative field, or PhD.
- Deep Agentic AI expertise with hands-on experience in multi-agent orchestration, tool use, memory, and guardrails using frameworks like LangGraph, AutoGen, or AWS AgentCore; proficiency in responsible AI tooling including AWS Clarify, Guardrails for Bedrock, model explainability, and bias detection.
- Strong ability to determine solution strategy and where to simplify or extend solutions for the best outcome.
- Expertise in architecting AI systems within highly regulated or security-sensitive environments (e.g., Financial Services, Healthcare, Public Sector).
Inclusivity at Amazon
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
Key skills/competency
- AI Solution Architect
- Generative AI
- Machine Learning
- Agentic Technologies
- AWS
- Cloud Architecture
- LLM Deployment
- Technical Leadership
- Customer Advisory
- Solution Design
Skills & topics
- AI Solution Architect
- Generative AI
- Machine Learning
- AWS
- Cloud Computing
- LLM
- Agentic AI
- Solution Architecture
- Deep Learning
- Artificial Intelligence
How to get hired
- Tailor your resume: Highlight your 7+ years of distributed application experience and 5+ years in enterprise customer-facing roles. Emphasize LLM deployment and AWS ecosystem expertise.
- Showcase AI/ML expertise: Detail your experience with GenAI, Agentic technologies, LLMs, and responsible AI tooling. Quantify your impact on customer projects.
- Prepare for technical interviews: Be ready to discuss complex distributed systems, cloud architecture patterns, and specific AWS services like SageMaker and Bedrock.
- Demonstrate leadership: Provide examples of technical leadership, customer advisory, and influencing stakeholders. Articulate your communication skills for diverse audiences.
Technical preparation
Master AWS AI services: Bedrock, SageMaker, AgentCore.,Practice LLM deployment scenarios (GPUs, TPUs).,Design secure, scalable AI architectures.,Implement RAG with vector stores and semantic search.
Behavioral questions
Describe a complex customer AI challenge.,How do you influence technical decisions?,Share experience advising C-level executives.,Explain a challenging project's technical strategy.
Frequently asked questions
- What are the primary responsibilities of an AI Solution Architect at AWS?
- The AI Solution Architect at AWS is responsible for building technical relationships with customers, advising them on adopting GenAI/ML and Agentic technologies, and ensuring they maximize their use of AWS services. This includes architecting solutions, providing technical recommendations, and acting as a liaison between customers and AWS product teams.
- What specific AWS services are important for this AI Solution Architect role?
- Key AWS services for this role include Bedrock, AgentCore, and SageMaker. Experience with setting up secure, private-network AI environments and implementing Retrieval-Augmented Generation using embeddings, vector stores, and semantic search optimization is highly valued.
- What is the expected experience level for an AI Solution Architect at AWS?
- The role requires a minimum of 7+ years in design, implementation, operations, or consulting with distributed applications, and 5+ years managing technical, enterprise customer-facing resources. Specific experience with LLM deployment and AWS ecosystems is also crucial.
- Does AWS offer accommodations for candidates with disabilities during the hiring process for the AI Solution Architect position?
- Yes, AWS is committed to inclusivity and offers workplace accommodations for candidates with disabilities. You can find more information and guidance on their careers site at https://amazon.jobs/content/en/how-we-hire/accommodations.
- What differentiates an AI Specialist Solution Architect (SSA) from a general Solution Architect at AWS?
- An AI Specialist Solution Architect (SSA) possesses deep, domain-specific expertise in AI and Machine Learning, focusing on advanced concepts and feature designs within AI adoption. They work with customers facing complex AI challenges, ensuring they leverage AWS for GenAI/ML and Agentic technologies.
- How can I best prepare for the technical interview for the AI Solution Architect role at AWS?
- To prepare for the technical interview, focus on demonstrating your hands-on experience with AWS AI services (Bedrock, SageMaker), LLM deployment on various hardware, and architecture design for scalable AI systems. Be ready to discuss best practices in security, cost, performance, and operational efficiency for AI workloads.