Solution Architect - Gen AI

Overview

The Solution Architect provides technical leadership and designs complex solution architectures that support business strategy and streamline technology-enabled workflows, with a focus on emerging technologies including AI, Generative AI, and machine learning.

What you'll do

• Lead discovery with business and technology partners to understand objectives, constraints, current-state systems, and integration points.
• Document current-state architecture and define target-state designs including system context diagrams, component designs, integration patterns, and data flows.
• Design and modernize applications into cloud-compatible or cloud-native architectures using microservices, serverless, and event-driven patterns where appropriate.
• Create strategies, roadmaps, and migration designs for transitioning applications and data workloads to cloud platforms.
• Design AI and ML-enabled solutions, including model integration into products and business processes, and patterns for scalable inference and low-latency serving where needed.
• Design Generative AI solution patterns, such as retrieval-augmented generation, tool and API integration, prompt and context management, and evaluation approaches.
• Define reference architectures for AI platforms and enabling capabilities, such as data pipelines, feature and embedding generation, vector storage, model endpoints, and integration with enterprise APIs.
• Define integration patterns for agent toolchains using standards and common approaches such as Model Context Protocol (MCP), skills and plugin manifest standards, function calling with JSON Schema tool definitions, OpenAPI-based tool specifications, and event-driven or message-based tool execution patterns.
• Ensure integrations address authentication and authorization, data minimization, auditability, and isolation boundaries.
• Incorporate security-by-design practices into architectures, including identity and access controls, encryption, secrets management, secure networking, and audit logging.
• Partner with governance and risk stakeholders to ensure responsible AI considerations are incorporated, including privacy, explainability, safety, compliance, and model risk controls as applicable.
• Drive alignment and adoption of proposed solutions by clearly communicating tradeoffs, risks, and value and obtaining stakeholder alignment and governance approvals.
• Support teams responsible for testing and validation and help triage and resolve design-related issues found during development, UAT, or production.
• Perform other duties as assigned.

What you'll need

• Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, Business, or equivalent practical experience.
• 5+ years of experience in agile software delivery environments with increasing architecture and design responsibility.
• Demonstrated experience designing distributed systems using microservices and or serverless patterns.
• Experience designing and integrating AI and ML capabilities into applications, including model serving considerations and data dependencies.
• Experience with one or more languages such as Java, Python, Node.js, or Scala.
• Experience with data persistence technologies across SQL and NoSQL.
• Experience with at least one major cloud provider, AWS, Azure, or Google Cloud, and core cloud design patterns.
• Working knowledge of CI/CD pipelines and DevOps practices, including automated testing and deployment automation.
• Strong communication skills and ability to translate business needs into clear technical direction.

Nice to have

• Hands-on experience with GenAI and LLM solutions, including retrieval-augmented generation, embeddings, evaluation, and production monitoring.
• Experience with AI and ML platforms or services, such as AWS SageMaker, Amazon Bedrock, Azure AI, Azure OpenAI, or Google Vertex AI.
• Familiarity with MCP-based integrations and related agent tool standards, including skills and plugin manifests, function calling schemas, and OpenAPI-defined tools, plus secure enterprise integration patterns.
• Container and orchestration experience, such as Docker and Kubernetes, and cloud container platforms like ECS or EKS.
• Experience with vector databases and search technologies and associated indexing and retrieval patterns.
• Experience with enterprise observability, including centralized logging, tracing, metrics, alerting, and operational readiness.
• Database and procedural development experience, including PL/SQL, and strong data modeling concepts.
• Familiarity with responsible AI practices, governance controls, and security considerations specific to AI systems.

Details

• Location: KA Bangalore.